Methods for designing, manufacturing, installing, and/or maintenance of roofing accessories and systems of use thereof

ABSTRACT

Systems and methods of the present disclosure enable automated roof planning using a processor. The processor receives a digital image of a roof of a structure and models each roof plane of the roof to generate a roof model. The processor determines dimensions of each roof plane based on the roof model. The processor retrieves roofing accessory data from a database, the roofing accessory data solar roofing accessory part identifiers and solar roofing accessory part performance characteristics for solar roofing accessories. The processor simulates multiple candidate roof layouts based on the dimensions of each roof plan and the solar roofing accessory parts and determines a utilization prediction for each candidate layout. Based on each utilization prediction, the processor determines a particular roof layout having selected solar roofing accessory parts, and generates a solar roof design, including a list of materials, for the particular roof layout.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 18/056,957, filed on Nov. 18, 2022, which claims priority toU.S. Provisional Application 63/281,391, filed on Nov. 19, 2021, whichis hereby incorporated by reference in their entirety.

FIELD OF TECHNOLOGY

The present disclosure generally relates to methods for designing,manufacturing, installing, and/or maintenance of roofing accessories andsystems of use thereof.

BACKGROUND OF TECHNOLOGY

Solar-based power generation has become an important tool for energygeneration and grid resiliency. Solar generation devices may bedesigned, manufactured, installed and/or maintained by various methods.

SUMMARY OF DESCRIBED SUBJECT MATTER

In some embodiments, the present disclosure provides an exemplarytechnically improved method that includes at least the following stepsof: receiving, by a processor, at least one digital image of a roof of astructure; modelling, by the processor, at least one roof plane of theroof in the at least one digital image to generate a roof plane model;determining, by the processor, a plurality of dimensions of the at leastone roof plane based at least in part on the roof plane model;retrieving, by the processor, roofing accessory data in a roofingaccessory database, where the roofing accessory data includes: aplurality of solar roofing accessory part identifiers, where each solarroofing accessory part identifier identifies each solar roofingaccessory part of the plurality of solar roofing accessory parts and aplurality of solar roofing accessory part performance characteristics,where each solar roofing accessory part performance characteristicassociated with each solar roofing accessory part of the plurality ofsolar roofing accessory parts; simulating, by the processor, a pluralityof candidate roof layouts for the at least one roof plane based at leastin part on: the plurality of dimensions of the at least one roof planeand the plurality of solar roofing accessory parts, where each candidateroof layout of the plurality candidate roof layouts includes a pluralityof roof segments, where the plurality of roof segments includes: aplurality of first type roof segments having a plurality of solarroofing accessory parts and a plurality of second type roof segmentshaving a plurality of non-solar roofing accessory parts; analyzing, bythe processor, the plurality of candidate roof layouts based at least inpart on the plurality of first type roof segments having the pluralityof solar roofing accessory parts and the plurality of second type roofsegments having the plurality of non-solar roofing accessory parts toassign a particular utilization prediction to each candidate roof layoutof the plurality of candidate roof layouts to generate a plurality ofutilization predictions, where the particular utilization prediction ofeach candidate roof layout is based at least in part on: at least oneinstallation metric for installing each candidate roof layout and atleast one solar roofing accessory part performance characteristicassociated with each solar roofing accessory part; determining, by theprocessor, a particular roof layout from the plurality of candidate rooflayouts based at least in part on the plurality of utilizationpredictions associated with the plurality of candidate roof layouts,where the particular roof layout includes: a plurality of selected solarroofing accessory parts associated with the plurality of solar roofingparts of the particular roof layout and a plurality of selectednon-solar roofing accessory parts of the plurality of non-solar roofingaccessory parts; generating, by the processor, a solar roof design basedat least in part on the particular roof layout, where the solar roofdesign includes a list of solar roofing accessory part identifiers,identifying each selected solar roofing accessory part of the pluralityof selected solar roofing accessory parts of the particular roof layoutso as to track each selected solar roofing accessory part during alifetime of a roof completed based at least in part on the solar roofdesign; and instructing, by the processor, at least one computing deviceto display the solar roof design.

In some embodiments, the present disclosure provides an exemplarytechnically improved system that includes at least the followingaccessories of: at least one processor configured to execute softwareinstructions. The software instructions, when executed, cause that leastone processor to perform steps to: receiving, by a processor, at leastone digital image of a roof of a structure; model at least one roofplane of the roof in the at least one digital image to generate a roofplane model; determine a plurality of dimensions of the at least oneroof plane based at least in part on the roof plane model; retrieveroofing accessory data in a roofing accessory database, where theroofing accessory data includes: a plurality of solar roofing accessorypart identifiers, where each solar roofing accessory part identifieridentifies each solar roofing accessory part of the plurality of solarroofing accessory parts and a plurality of solar roofing accessory partperformance characteristics, where each solar roofing accessory partperformance characteristic associated with each solar roofing accessorypart of the plurality of solar roofing accessory parts; simulate aplurality of candidate roof layouts for the at least one roof planebased at least in part on: the plurality of dimensions of the at leastone roof plane and the plurality of solar roofing accessory parts, whereeach candidate roof layout of the plurality candidate roof layoutsincludes a plurality of roof segments, where the plurality of roofsegments includes: a plurality of first type roof segments having aplurality of solar roofing accessory parts and a plurality of secondtype roof segments having a plurality of non-solar roofing accessoryparts; analyze the plurality of candidate roof layouts based at least inpart on the plurality of first type roof segments having the pluralityof solar roofing accessory parts and the plurality of second type roofsegments having the plurality of non-solar roofing accessory parts toassign a particular utilization prediction to each candidate roof layoutof the plurality of candidate roof layouts to generate a plurality ofutilization predictions, where the particular utilization prediction ofeach candidate roof layout is based at least in part on: at least oneinstallation metric for installing each candidate roof layout and atleast one solar roofing accessory part performance characteristicassociated with each solar roofing accessory part; determine aparticular roof layout from the plurality of candidate roof layoutsbased at least in part on the plurality of utilization predictionsassociated with the plurality of candidate roof layouts, where theparticular roof layout includes: a plurality of selected solar roofingaccessory parts associated with the plurality of solar roofing parts ofthe particular roof layout and a plurality of selected non-solar roofingaccessory parts of the plurality of non-solar roofing accessory parts;generate a solar roof design based at least in part on the particularroof layout, where the solar roof design includes a list of solarroofing accessory part identifiers, identifying each selected solarroofing accessory part of the plurality of selected solar roofingaccessory parts of the particular roof layout so as to track eachselected solar roofing accessory part during a lifetime of a roofcompleted based at least in part on the solar roof design; and instructat least one computing device to display the solar roof design.

One or more embodiments of systems and methods described herein furtherinclude: determining, by the processor, full-device placements in the atleast one first type roof segment based at least in part on theplurality of solar roofing accessory parts and the dimensions of the atleast one roof plane, where the full-device placements represent a firstarrangement of the plurality of solar roofing accessory parts that fitwithin in the at least one first type roof segment; determining, by theprocessor, partial-device placements in the at least one second typeroof segment based at least in part on the plurality of solar roofingaccessory parts and the dimensions of the at least one roof plane; andwhere the partial-device placements represent positions on the at leastone roof plane that fit a portion of the plurality of solar roofingaccessory parts.

One or more embodiments of systems and methods described herein furtherinclude where the plurality of non-solar roofing accessory parts includeasphalt shingles.

One or more embodiments of systems and methods described herein furtherinclude: generating, by the processor, a bill-of-materials including alist of parts, including solar roofing accessory parts, non-solarroofing accessory parts, any other parts, or a combination thereof. Incertain embodiments, the bill-of-materials may be included with thesolar roof design generated by the systems and methods. The systems andmethods may generate estimations of a labor cost and an accessory costassociated with installing the plurality of selected solar roofingaccessory parts, non-solar roofing accessory parts, or a combinationthereof, according to the solar roof design. In one or more embodiments,the estimations may serve as evaluation criteria by the systems andmethods to facilitate selection of a solar roof design out of a pool ofcandidate solar roof designs.

One or more embodiments of systems and methods described herein furtherinclude: determining, by the processor, an installation time needed toinstall each selected solar roofing accessory part of the plurality ofselected solar roofing accessory parts according to the solar roofdesign; and generating, by the processor, a bill-of-materials includinga list of the selected solar roofing accessory parts according to thesolar roof design.

One or more embodiments of systems and methods described herein furtherinclude: determining, by the processor, a quantity of the plurality ofnon-solar roofing accessory parts of the solar roof design based atleast in part on particular roof layout; and generating, by theprocessor, a bill-of-materials representing the quantity of theplurality of non-solar roofing accessory parts of the solar roof design.

One or more embodiments of systems and methods described herein furtherinclude where each solar roofing accessory part performancecharacteristic associated with each solar roofing accessory part of theplurality of solar roofing accessory parts includes a part-specificsolar efficiency metric.

One or more embodiments of systems and methods described herein furtherinclude: receiving, by the processor, the at least one digital imageincluding light detection and ranging (LiDAR) measurements of the roofof the structure; and generating, by the processor, a three-dimensionalmodel of the roof based at least in part on the LiDAR measurements.

One or more embodiments of systems and methods described herein furtherinclude: determining, by the processor, a geographic location associatedwith the structure; determining, by the processor, a geographicorientation of the at least one roof plane based at least in part on thegeographic location and the at least one digital image; and scoring, bythe processor, the plurality of candidate roof layouts based at least inpart on based at least in part on the geographic orientation and theplurality of selected solar roofing accessory parts.

One or more embodiments of systems and methods described herein furtherinclude: determining, by the processor, at least one obstruction overthe at least one roof plane based at least in part on the at least onedigital image; and analyzing, by the processor, the plurality ofcandidate roof layouts based at least in part on based at least in parton the at least one obstruction and the plurality of selected solarroofing accessory parts.

One or more embodiments of systems and methods described herein furtherinclude: receiving, by the at least one processor, an updated solarroofing accessory part performance characteristic associated with aparticular solar roofing accessory part of the plurality of solarroofing accessory parts, where the updated solar roofing accessory partperformance characteristic associated with a particular solar roofingaccessory part includes at least one user input indicating a change tothe solar roofing accessory part performance characteristic associatedwith the particular solar roofing accessory part; and updating, by theat least one processor, a record associated with the particular solarroofing accessory part to indicate the updated solar roofing accessorypart performance characteristic; and where the record is stored in aroofing accessory database.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present disclosure can be further explainedwith reference to the attached drawings, wherein like structures arereferred to by like numerals throughout the several views. The drawingsshown are not necessarily to scale, with emphasis instead generallybeing placed upon illustrating the principles of the present disclosure.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a representativebasis for teaching one skilled in the art to variously employ one ormore illustrative embodiments.

FIG. 1A is a block diagram of an exemplary computer-based system forsolar roof design in accordance with one or more embodiments of thepresent disclosure.

FIG. 1B is a block diagram of an exemplary computer-based systemconfigured for facilitating exemplary method(s) for designing,manufacturing, installing, and/or maintenance of a solar roof inaccordance with one or more embodiments of the present disclosure.

FIG. 2 is a block diagram of an exemplary computer-based system for asolar roof design using automated structural modelling in accordancewith one or more embodiments of the present disclosure.

FIG. 3 is a block diagram of an exemplary computer-based system forsolar roof design using structure-specific and accessory-specificmodelling and analysis based on per-accessory tracking data inaccordance with one or more embodiments of the present disclosure.

FIG. 4 is a block diagram of an exemplary computer-based system forsolar roof design including automated design plan prediction inaccordance with one or more embodiments of the present disclosure.

FIG. 5 depicts a block diagram of an exemplary computer-based system 500for solar roof design in accordance with one or more embodiments of thepresent disclosure.

FIG. 6 depicts a block diagram of another exemplary computer-basedsystem and platform 600 for facilitating solar roof design in accordancewith one or more embodiments of the present disclosure.

FIG. 7 illustrates schematics of exemplary implementations of a cloudcomputing/architecture(s) which may be utilized to facilitate solar roofdesign in accordance with one or more embodiments of the presentdisclosure.

FIG. 8 illustrates schematics of exemplary implementations of the cloudcomputing/architecture(s) in which solar roof designs may bespecifically configured to be generated in accordance with one or moreembodiments of the present disclosure.

FIG. 9 illustrates a method for facilitating generation of a solar roofdesign according to one or more embodiments of the present disclosure.

DETAILED DESCRIPTION

Various detailed embodiments of the present disclosure, taken inconjunction with the accompanying figures, are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely illustrative. In addition, each of the examples given inconnection with the various embodiments of the present disclosure isintended to be illustrative, and not restrictive.

Throughout the specification, the following terms take the meaningsexplicitly associated herein, unless the context clearly dictatesotherwise. The phrases “in one embodiment” and “in some embodiments” asused herein do not necessarily refer to the same embodiment(s), thoughit may. Furthermore, the phrases “in another embodiment” and “in someother embodiments” as used herein do not necessarily refer to adifferent embodiment, although it may. Thus, as described below, variousembodiments may be readily combined, without departing from the scope orspirit of the present disclosure.

In addition, the term “based on” is not exclusive and allows for beingbased on additional factors not described, unless the context clearlydictates otherwise. In addition, throughout the specification, themeaning of “a,” “an,” and “the” include plural references. The meaningof “in” includes “in” and “on.”

As used herein, the terms “and” and “or” may be used interchangeably torefer to a set of items in both the conjunctive and disjunctive in orderto encompass the full description of combinations and alternatives ofthe items. By way of example, a set of items may be listed with thedisjunctive “or”, or with the conjunction “and.” In either case, the setis to be interpreted as meaning each of the items singularly asalternatives, as well as any combination of the listed items.

FIGS. 1 through 8 illustrate systems and methods of automated structuremodelling, design and part tracking. The following embodiments providetechnical solutions and technical improvements that overcome technicalproblems, drawbacks and/or deficiencies in the technical fieldsinvolving computer aided design and manufacturing. As explained in moredetail, below, technical solutions and technical improvements hereininclude aspects of improved per-accessory tracking and modelling forimproved structure design using computer-aided and machinelearning-based techniques. By improving the accuracy and precision ofthe design tools using per-accessory and structural data, powergeneration by the modelled design may be improved such that powergeneration is improved, structural resiliency is improved, and gridresiliency is improved. Based on such technical features, furthertechnical benefits become available to users and operators of thesesystems and methods. Moreover, various practical applications of thedisclosed technology are also described, which provide further practicalbenefits to users and operators that are also new and usefulimprovements in the art.

FIG. 1A is a block diagram of an exemplary computer-based system forsolar roof design in accordance with one or more embodiments of thepresent disclosure.

In some embodiments, roof and roofing system design and constructioninvolves choosing the appropriate materials and roofing accessories forthe roof. In some embodiments, variations in roofing accessories maymake one roofing accessory a more optimal design than another, such as,e.g., installing shingles directly on a roofing deck or installing anunderlayment on the roofing deck with the shingles installed on theunderlayment. Moreover, there may be variations in performance and partcharacteristics of individual parts of the same roofing accessory type.Such variation may be improved or degrade the performance of a roofingsystem on a roof.

In some embodiments, a data storage 112 may, in a roofing accessorydatabase 114, record performance characteristics and/or other partcharacteristics of individual roofing accessory parts.

In some embodiments, the roofing accessory database 114 may include oneor more local and/or remote data storage solutions such as, e.g., localhard-drive, solid-state drive, flash drive, database or other local datastorage solutions or any combination thereof, and/or remote data storagesolutions such as a server, mainframe, database or cloud services,distributed database or other suitable data storage solutions or anycombination thereof. In some embodiments, the roofing accessory database114 may include, e.g., a suitable non-transient computer readable mediumsuch as, e.g., random access memory (RAM), read only memory (ROM), oneor more buffers and/or caches, among other memory devices or anycombination thereof.

In some embodiments, “database” refers to an organized collection ofdata, stored, accessed or both electronically from a computer system.The database may include a database model formed by one or more formaldesign and modeling techniques. The database model may include, e.g., anavigational database, a hierarchical database, a network database, agraph database, an object database, a relational database, anobject-relational database, an entity-relationship database, an enhancedentity-relationship database, a document database, anentity-attribute-value database, a star schema database, or any othersuitable database model and combinations thereof. For example, thedatabase may include database technology such as, e.g., a centralized ordistributed database, cloud storage platform, decentralized system,server or server system, among other storage systems. In someembodiments, the database may, additionally or alternatively, includeone or more data storage devices such as, e.g., a hard drive,solid-state drive, flash drive, or other suitable storage device. Insome embodiments, the database may, additionally or alternatively,include one or more temporary storage devices such as, e.g., arandom-access memory, cache, buffer, or other suitable memory device, orany other data storage solution and combinations thereof.

Depending on the database model, one or more database query languagesmay be employed to retrieve data from the database. Examples of databasequery languages may include: JSONiq, LDAP, Object Query Language (OQL),Object Constraint Language (OCL), PTXL, QUEL, SPARQL, SQL, XQuery,Cypher, DMX, FQL, Contextual Query Language (CQL), AQL, among suitabledatabase query languages.

The database may include one or more software, one or more hardware, ora combination of one or more software and one or more hardwarecomponents forming a database management system (DBMS) that interactswith users, applications, and the database itself to capture and analyzethe data. The DBMS software additionally encompasses the core facilitiesprovided to administer the database. The combination of the database,the DBMS and the associated applications may be referred to as a“database system”.

In some embodiments, the roofing accessory database 114 may beconfigured interact and/or to store data in one or more private and/orprivate-permissioned cryptographically-protected, distributed databasedsuch as, without limitation, a blockchain (distributed ledgertechnology), Ethereum (Ethereum Foundation, Zug, Switzerland), and/orother similar distributed data management technologies. For example, asutilized herein, the distributed database(s), such as distributedledgers ensure the integrity of data by generating a chain of datablocks linked together by cryptographic hashes of the data records inthe data blocks. For example, a cryptographic hash of at least a portionof data records within a first block, and, in some cases, combined witha portion of data records in previous blocks is used to generate theblock address for a new digital identity block succeeding the firstblock. As an update to the data records stored in the one or more datablocks, a new data block is generated containing respective updated datarecords and linked to a preceding block with an address based upon acryptographic hash of at least a portion of the data records in thepreceding block. In other words, the linked blocks form a blockchainthat inherently includes a traceable sequence of addresses that can beused to track the updates to the data records contained therein. Thelinked blocks (or blockchain) may be distributed among multiple networknodes within a computer network such that each node may maintain a copyof the blockchain. Malicious network nodes attempting to compromise theintegrity of the database must recreate and redistribute the blockchainfaster than the honest network nodes, which, in most cases, iscomputationally infeasible. In other words, data integrity is guaranteedby the virtue of multiple network nodes in a network having a copy ofthe same blockchain. In some embodiments, as utilized herein, a centraltrust authority for sensor data management may not be needed to vouchfor the integrity of the distributed database hosted by multiple nodesin the network.

In some embodiments, a distributed blockchain-type ledger implementationof the roofing accessory database 114 may be configured to utilize smartcontracts, which are computer processes that facilitate, verify and/orenforce negotiation and/or performance of one or more particularactivities among users/parties. For example, an exemplary smart contractmay be configured to be partially or fully self-executing and/orself-enforcing. In some embodiments, the exemplary inventiveasset-tokenized distributed blockchain-type ledger implementations ofthe present disclosure may utilize smart contract architecture that canbe implemented by replicated registries of the roofing accessorydatabase 114 and contract execution using cryptographic hash chains andByzantine fault tolerant replication. For example, each node in apeer-to-peer network or blockchain distributed network may act as aroofing accessory database 114 or a part of the roofing accessorydatabase 114, thereby executing changes of status, performance,condition or other characteristic of each roofing accessory partaccording to sets of predetermined rules that govern transactions on thenetwork. For example, each node may also check the work of other nodesand in some cases, as noted above, function as miners or validators.

In some embodiments, the terms “cloud,” “Internet cloud,” “cloudcomputing,” “cloud architecture,” and similar terms correspond to atleast one of the following: (1) a large number of computers connectedthrough a real-time communication network (e.g., Internet); (2)providing the ability to run a program or application on many connectedcomputers (e.g., physical machines, virtual machines (VMs)) at the sametime; (3) network-based services, which appear to be provided by realserver hardware, and are in fact served up by virtual hardware (e.g.,virtual servers), simulated by software running on one or more realmachines (e.g., allowing to be moved around and scaled up (or down) onthe fly without affecting the end user). The aforementioned examplesare, of course, illustrative and not restrictive.

In some embodiments, the roofing accessory database 114 may includeperformance data and/or condition data for each roofing accessory part,where each roofing accessory part refers to an individual item, such asa particular shingle, tile, solar shingle, solar panel, active shingle(e.g., a shingle having electronic components integrated therein), ridgecap, ridge vent, roof vent, underlayment, waterproof membrane, antenna,and any other roofing accessory.

In some embodiments, the roofing accessory database 114 may index arecord associated with a particular roofing accessory part 116 accordingto an associated roofing accessory part identifier. In some embodiments,the roofing accessory part identifier may include, e.g., a part/modelnumber and/or name, a serial number, or other identifier identifying theparticular roofing accessory part 116 or any combination thereof.

In some embodiments, the performance data may include, e.g., ratedtemperature range, rated load capacity, rated impact limit, ratedefficiency, rated waterproofing measure, measured temperature range,measured load capacity, measured impact limit, measured efficiency,measured/tested waterproofing measure, average defect(s) quantity,measured defect(s) quantity, quality control results, among otherperformance data or any combination thereof. In some embodiments, theperformance data may include mechanical performance, such as, e.g.,rated load capacity, rated weatherproofing capacity (e.g., waterproofingand/or wind proofing, etc.), rated insulation performance, ratedtemperature range, rated lifetime, measured load capacity, measuredweatherproofing capacity (e.g., waterproofing and/or wind proofing,etc.), measured insulation performance, measured temperature range,average defect(s) quantity, measured defect(s) quantity, time-basedperformance change, usage-based performance change, quality controlresults, or other mechanical accessory performance metric or anycombination thereof.

In some embodiments, where the particular roofing accessory part 116 isan active roofing accessory, such as a solar roofing accessory, theperformance data may include, e.g., rated solar efficiency, rated powerefficiency, measure solar efficiency, measured power efficiency, ratedpeak solar power generation, measured peak solar power generation, ratedaverage solar power generation, measured average solar power generation,rated voltage, measured voltage, rated amperage, measured amperage,time-based performance change, usage-based performance change, qualityand/or performance binning, Local and Regional Permitting data sets,such as required foot setbacks or activation requirements, or otheractive accessory performance metric or any combination thereof. In someembodiments, the performance data may include electronic performance,such as, e.g., rated processing speed, rated throughput, ratedefficiency, measured processing speed, measured throughput, measuredefficiency, average defect(s) quantity, measured defect(s) quantity,time-based performance change, usage-based performance change, qualityand/or performance binning, quality control results, or other electronicaccessory performance metric or any combination thereof.

In some embodiments, the performance and/or mechanical data of theparticular roofing accessory part 116 may be tracked through time basedon updates to the performance data and/or mechanical data. In someembodiments, to reference and/or update the record associated with theparticular roofing accessory part 116, a user may employ a usercomputing device 101 to provide the associated roofing accessory partidentifier of the particular roofing accessory part 116.

In some embodiments, the user computing device 101 may include or beincorporated, partially or entirely into at least one personal computer(PC), laptop computer, ultra-laptop computer, tablet, smartphone,portable computer, handheld computer, palmtop computer, personal digitalassistant (PDA), cellular telephone, combination cellular telephone/PDA,television, smart device (e.g., smart phone, smart tablet or smarttelevision), mobile internet device (MID), messaging device, datacommunication device, edge computing device, Internet-of-Things (IoT)device, and so forth.

In some embodiments, the user may manually input the roofing accessorypart identifier, e.g., via alphanumeric input using a suitable inputdevice, such as a keyboard, mouse, touch-enabled display, gesturecontrol, etc. In some embodiments, the user computing device 101 mayautomatically obtain the roofing accessory part identifier via animaging device 115. In some embodiments, the roofing accessory partidentifier may include, e.g., a machine-readable code 117 encoding theroofing accessory part identifier. In some embodiments, themachine-readable code 117 may include, e.g., a barcode, a quickreference (QR) code, or other suitable visible encoding. In someembodiments, the user computing device 101 may automatically obtain theroofing accessory part identifier via a wireless reader device, such as,e.g., a radio-frequency identification (RFID) reader, a near fieldcommunication (NFC) reader, a Bluetooth® receiver, a WiFi receiver, orother suitable wireless reader device configured to read data encoded ina wireless transmission device. In some embodiments, the wirelesstransmission device may include, e.g., an RFID tag (passive and/oractive), an NFC tag, a Bluetooth® transmitter, a WiFi transmitter, aZigbee transmitter, a Z-Wave transmitter, an ORAN transmitter, anUltra-Wide Band (UWB) transmitter, a LoRaWAN transmitter or othersuitable wireless transmission device configured to convey a wirelesssignal encoding the roofing accessory part identifier, or anycombination thereof. In some embodiments, purely for illustration, theuser computing device 101 is depicted as obtaining the roofing accessorypart identifier via the image capture device 115 to capture an image ofthe machine-readable code 117.

In some embodiments, the user computing device 101 may automaticallydetect the presence of the particular roofing accessory part 116 andautomatically read the machine-readable code 117, e.g., via NFC, RFID,Bluetooth, WiFi, Zigbee, Z-Wave, LoRaWAN, ORAN, etc. Upon reading themachine-readable code 117, the user computing device 101 mayautomatically register the particular roofing accessory part 116 withthe roofing accessory database 114.

Alternatively, or additionally, in some embodiments, the roofingaccessory part identifier may be automatically registered with theroofing accessory database 114. In some embodiments, the particularroofing accessory part 116 may include, e.g., an embedded computingdevice, such as an edge computing device, Internet-of-Things (IoT)device. The embedded computing device may include, e.g., a wirelesscommunication connection, such as, e.g., WiFi, Zigbee, Z-Wave, 5G, 4G,3G, or other data and/or cellular connection. As a result, uponinstallation (e.g., upon being provided with power or a powerconnection, or upon manual activation during installation), theparticular roofing accessory part 116 may use the embedded computingdevice to connect with the roofing accessory database 114, e.g., using asuitable wireless communication connection over a suitable network(e.g., WiFi network, Ethernet network, Local Area Network (LAN), IoT orsmart home network, the Internet, or other suitable network or anycombination thereof).

In some embodiments, upon receiving the roofing accessory partidentifier, the user computing device 101 and/or the embedded computingdevice may upload the roofing accessory part identifier using, e.g., asuitable database query, to identify and access the record associatedwith the particular roofing accessory part 116 in the roofing accessorydatabase 114. In some embodiments, the user computing device 101 mayupload the roofing accessory part identifier via, e.g., a suitablenetwork. In some embodiments, the network may include a suitable networktype, such as, e.g., a local-area network (LAN), a wide-area network(WAN) or other suitable type. In some embodiments, a LAN may connectcomputers and peripheral devices in a physical area, such as a businessoffice, laboratory, or college campus, by means of links (wires,Ethernet cables, fiber optics, wireless such as Wi-Fi, etc.) thattransmit data. In some embodiments, a LAN may include two or morepersonal computers, printers, and high-capacity disk-storage devicescalled file servers, which enable each computer on the network to accessa common set of files. LAN operating system software, which interpretsinput and instructs networked devices, may enable communication betweendevices to: share the printers and storage equipment, simultaneouslyaccess centrally located processors, data, or programs (instructionsets), and other functionalities. Devices on a LAN may also access otherLANs or connect to one or more WANs. In some embodiments, a WAN mayconnect computers and smaller networks to larger networks over greatergeographic areas. A WAN may link the computers by means of cables,optical fibers, or satellites, or other wide-area connection means. Insome embodiments, an example of a WAN may include the Internet.

In some embodiments, the user computing device 101 may upload theroofing accessory part identifier using a suitable network communicationtechnology, including, e.g., a suitable routing/communication protocol(e.g., hypertext transport protocol (HTTP), etc.), and/or a suitableinterfacing technology, such as, e.g., an application programminginterface (API). In some embodiments, the API may include, e.g., acomputing interface that defines interactions between multiple softwareintermediaries. An “application programming interface” or “API” definesthe kinds of calls or requests that can be made, how to make the calls,the data formats that should be used, the conventions to follow, amongother requirements and constraints. An “application programminginterface” or “API” can be entirely custom, specific to a component, ordesigned based on an industry-standard to ensure interoperability toenable modular programming through information hiding, allowing users touse the interface independently of the implementation.

In some embodiments, in response to the roofing accessory partidentifier, the roofing accessory database 114 may return the recordassociated with the particular roofing accessory part 116. In someembodiments, user computing device 101 may then present a suitablegraphical user interface (GUI) for presenting the record to the user. Insome embodiments, the GUI may provide user interface elements forviewing and/or editing characteristics stored in the record, including,e.g., the performance data and/or the mechanical data associated withthe particular roofing accessory part 116.

In some embodiments, where the GUI provides user interface elements forediting characteristics of the particular roofing accessory part 116,the user may select and/or input modifications to the characteristics,such as, e.g., a current performance according to most recentmeasurements, a current mechanical condition, a current state (e.g.,brand new, installed, uninstalled, reused/reinstalled, etc.), amongother characteristics or any combination thereof. In some embodiments,each edit may be appended to the record in order to provide a timelineof the lifetime of the particular roofing accessory part 116.

For example, when selected for a particular roofing system, the user mayscan the machine-readable indicia 117 with the imaging device 115 viathe user computing device 101. In some embodiments, the user computingdevice 101 may upload the roofing accessory part identifier encoded inthe machine-readable indicia 117 to access the record and thecharacteristics stored therein. The user may then edit the record toindicate that a status as installed upon installing the particularroofing accessory part 116 in the roofing system. In some embodiments,the user may then re-scan the machine-readable indicia 117 to access therecord and update changes to the characteristics including performancedata and/or mechanical data, or any change in status, such as a time atwhich the particular roofing accessory part 116 is uninstalled from theroofing system (e.g., to be replaced with a new or different roofingaccessory part). In some embodiments, where the particular roofingaccessory part 116 may still be employed in a roofing system based onthe most recent performance data and/or mechanical data uponuninstallation based on the record, the particular roofing accessorypart 116 may be recycled and selected for a new roofing system (e.g., tobe sold at a reduced cost or reused by the owner of the original roofingsystem, etc.). Thus, the particular roofing accessory part 116 may berecycled one or more times, with each status change including eachinstallation and each uninstallation recorded in the record stored inthe roofing accessory database 114. Accordingly, the roofing accessorydatabase 114 may record a full lifecycle of each roofing accessory part,including solar roofing accessory parts, active roofing accessory partsand/or inactive roofing accessory parts.

FIG. 1B is a block diagram of an exemplary computer-based system forsolar roof design in accordance with one or more embodiments of thepresent disclosure.

In some embodiments, in response to a solar roof design request 103, asolar roof design system 100 may produce solar roof designs 105 based ona predicted performance of a particular roof layout using the roofingaccessory database 114 of data for roofing accessories includingper-accessory specifications and performance data for each roofingaccessory available. Such roofing accessories include one or more, e.g.,solar roofing accessory(s) (e.g., solar panel(s), solar shingle(s),solar panel(s)/shingle(s) parts, electrical accessory(s), etc.),shingle(s), underlayment(s), waterproof membrane(s), decking, roofingaccessory(s)/accessory(ies) having integrated and/or embedded electronicaccessories, antenna(s), roofing cap(s), laminate roofingaccessory(ies), roofing sheet(s), ridge cap(s), ridge vent(s), roofingframe(s) and the like, or any combination thereof.

Some embodiments of the present disclosure relate to at least oneroofing accessory. Some embodiments of the present disclosure include aplurality of roofing accessories. Some embodiments of the presentdisclosure include at least three roofing accessories. Some embodimentsof the present disclosure include at least five roofing accessories.Some embodiments of the present disclosure include at least ten roofingaccessories. Some embodiments of the present disclosure include at leastfifty roofing accessories. Some embodiments of the present disclosureinclude at least one hundred roofing accessories. Some embodiments ofthe present disclosure include at least one-thousand roofingaccessories.

In some embodiments, there are 1 to 10,000 roofing accessories. In someembodiments there are 1 to 5000 roofing accessories. In someembodiments, there are 1 to 1000 roofing accessories. In someembodiments, there are 1 to 100 roofing accessories. In someembodiments, there are 1 to 50 roofing accessories. In some embodiments,there are 1 to 25 roofing accessories. In some embodiments, there are 1to 10 roofing accessories. In some embodiments, there are 1 to 5 roofingaccessories. In some embodiments, there are 1 to 2 roofing accessories.

In some embodiments, there are 2 to 10,000 roofing accessories. In someembodiments, there are 5 to 10,000 roofing accessories. In someembodiments, there are 10 to 10,000 roofing accessories. In someembodiments, there are 50 to 10,000 roofing accessories. In someembodiments, there are 100 to 10,000 roofing accessories. In someembodiments, there are 500 to 10,000 roofing accessories. In someembodiments, there are 1000 to 10,000 roofing accessories. In someembodiments, there are 5000 to 10,000 roofing accessories.

In some embodiments, there are 2 to 5000 roofing accessories. In someembodiments, there are 5 to 1000 roofing accessories. In someembodiments, there are 10 to 5000 roofing accessories. In someembodiments, there are 50 to 100 roofing accessories. In someembodiments, there are 60 to 90 roofing accessories. In someembodiments, there are 70 to 80 roofing accessories.

Non-limiting examples of the at least one electronic accessory of the atleast one roofing accessory include: at least one antenna, at least onesolar array, at least one battery, at least one computing device, atleast one controller, at least one processor, the like, or anycombination thereof. The at least one electronic accessory may alsoinclude one or more processors, microprocessors, circuits, circuitelements (e.g., transistors, resistors, capacitors, inductors, and soforth), integrated circuits, application specific integrated circuits(ASIC), programmable logic devices (PLD), digital signal processors(DSP), field programmable gate array (FPGA), logic gates, registers,semiconductor device, chips, microchips, chip sets, and so forth. Insome embodiments, the one or more processors may be implemented as aComplex Instruction Set Computer (CISC) or Reduced Instruction SetComputer (RISC) processors; x86 instruction set compatible processors,multi-core, or any other microprocessor or central processing unit(CPU). In various implementations, the one or more processors may bedual-core processor(s), dual-core mobile processor(s), and so forth.

In some embodiments, the roofing accessory database 114 may includeperformance data for each roofing accessory, where each roofingaccessory refers to a part/model number and/or name, a serial number fora physical accessory, or any combination thereof. In some embodiments,the performance data may include solar performance, such as, e.g., ratedtemperature range, rated load capacity, rated impact limit, ratedefficiency, measured temperature range, measured load capacity, measuredimpact limit, measured efficiency, average defect(s) quantity, measureddefect(s) quantity, time-based performance change, usage-basedperformance change, quality and/or performance binning, quality controlresults, or other solar accessory performance metric or any combinationthereof. In some embodiments, the performance data may includeelectronic performance, such as, e.g., rated processing speed, ratedthroughput, rated efficiency, measured processing speed, measuredthroughput, measured efficiency, average defect(s) quantity, measureddefect(s) quantity, time-based performance change, usage-basedperformance change, quality and/or performance binning, quality controlresults, or other electronic accessory performance metric or anycombination thereof. In some embodiments, the performance data mayinclude mechanical performance, such as, e.g., rated load capacity,rated weatherproofing capacity (e.g., waterproofing and/or windproofing, etc.), rated insulation performance, rated temperature range,rated lifetime, measured load capacity, measured weatherproofingcapacity (e.g., waterproofing and/or wind proofing, etc.), measuredinsulation performance, measured temperature range, average defect(s)quantity, measured defect(s) quantity, time-based performance change,usage-based performance change, quality control results, or othermechanical accessory performance metric or any combination thereof.

In some embodiments, the roofing accessory database 114 may includemanufacturing-related data for each roofing accessory. In someembodiments, the manufacturing-related data may include, e.g.,temperature during manufacturing, humidity during manufacturing, time ofmanufacture, time and/or duration to complete manufacturing, line speed,process information, present during manufacturing, quality assurancestatus, production run identifier, among manufacturing-related data orany combination thereof.

In some embodiments, the solar roof design system 100 may produce solarroof designs 105 including, e.g., an integrated solar shingle roofingsystem, a comprehensive material and accessory list for a full roofingsystem, time and cost projections, among other solar roof design data orany combination thereof. In some embodiments, the solar roof designsystem 100 may leverage the roofing accessory database 114 and userinput provided in the solar roof design request 103 to determine anoptimized solar roof design 105 for the solar roof design request 103.In some embodiments, an optimized solar roof design 105 may include,e.g., an optimization based on solar efficiency maximization, peak powergeneration maximization, average power generation maximization, minimumpower generation maximization, cost minimization, construction timeminimization, lifespan maximization, or any other suitable solar roofoptimization metric or suitable balancing thereof. Accordingly, in someembodiments, the solar roof design system 100 may minimize cost andwaste for the roofing contractor by optimizing the roofing systemdesign, identifying the amount of material needed, and providing thecontractor with guidance on where to place the material whenconstructing the roof to maximize solar production and minimize labor,material waste based on data about each solar and non-solar roofingaccessory and/or the specific characteristics thereof.

In some embodiments, the solar roof design system 100 may receive thesolar roof design request 103 in response to user input via the usercomputing device 101. In some embodiments, the solar roof design request103 may include, for example, but not limited to, a physical structureidentifier selected and/or input by a user via the user computing device101, roof-related data represented within the solar roof design request103 itself as selected and/or input by a user via the user computingdevice 101, or any combination thereof. In some embodiments, the solarroof design system 100 may be a part of the user computing device 101.Thus, the solar roof design system 100 may include hardware and softwareaccessories including, e.g., user computing device 101 hardware andsoftware, cloud or server hardware and software, or a combinationthereof.

In some embodiments, the solar roof design system 100 may includehardware accessories such as a processor 111, which may include local orremote processing accessories. In some embodiments, the processor 111may include any type of data processing capacity, such as a hardwarelogic circuit, for example an application specific integrated circuit(ASIC) and a programmable logic, or such as a computing device, forexample, a microcomputer or microcontroller that include a programmablemicroprocessor. In some embodiments, the processor 111 may includedata-processing capacity provided by the microprocessor. In someembodiments, the microprocessor may include memory, processing,interface resources, controllers, and counters. In some embodiments, themicroprocessor may also include one or more programs stored in memory.

Similarly, the solar roof design system 100 may include the storage 112,such as one or more local and/or remote data storage solutions such as,e.g., local hard-drive, solid-state drive, flash drive, database orother local data storage solutions or any combination thereof, and/orremote data storage solutions such as a server, mainframe, database orcloud services, distributed database or other suitable data storagesolutions or any combination thereof. In some embodiments, the storage112 may include, e.g., a suitable non-transient computer readable mediumsuch as, e.g., random access memory (RAM), read only memory (ROM), oneor more buffers and/or caches, among other memory devices or anycombination thereof.

In some embodiments, the terms “cloud,” “Internet cloud,” “cloudcomputing,” “cloud architecture,” “edge compute,” “Internet-of-Things,”“IoT,” and similar terms correspond to at least one of the following:(1) a large number of computers connected through a real-timecommunication network (e.g., Internet); (2) providing the ability to runa program or application on many connected computers (e.g., physicalmachines, virtual machines (VMs)) at the same time; (3) network-basedservices, which appear to be provided by real server hardware, and arein fact served up by virtual hardware (e.g., virtual servers), simulatedby software running on one or more real machines (e.g., allowing to bemoved around and scaled up (or down) on the fly without affecting theend user). The aforementioned examples are, of course, illustrative andnot restrictive.

In some embodiments, the solar roof design system 100 may implementcomputer engines including, e.g., a roof modelling engine 120 to modelthe roof of a structure, a solar optimization engine 130 to model andoptimize roof layouts, a design plan engine 140 to generate a roofdesign plan based on the roof layouts and a material selection engine150 to generate a list of materials and/or tools for constructing theroof design plan. In some embodiments, the terms “computer engine” and“engine” identify at least one software accessory and/or a combinationof at least one software accessory and at least one hardware accessorywhich are designed/programmed/configured to manage/control othersoftware and/or hardware accessories (such as the libraries, softwaredevelopment kits (SDKs), objects, etc.).

Examples of hardware elements may include processors, microprocessors,circuits, circuit elements (e.g., transistors, resistors, capacitors,inductors, and so forth), integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), logicgates, registers, semiconductor device, chips, microchips, chip sets,and so forth. In some embodiments, the one or more processors may beimplemented as a Complex Instruction Set Computer (CISC) or ReducedInstruction Set Computer (RISC) processors; x86 instruction setcompatible processors, multi-core, or any other microprocessor orcentral processing unit (CPU). In various implementations, the one ormore processors may be dual-core processor(s), dual-core mobileprocessor(s), and so forth.

Examples of software may include software accessories, programs,applications, computer programs, application programs, system programs,machine programs, operating system software, middleware, firmware,software modules, routines, subroutines, functions, methods, procedures,software interfaces, application program interfaces (API), instructionsets, computing code, computer code, code segments, computer codesegments, words, values, symbols, or any combination thereof.Determining whether an embodiment is implemented using hardware elementsand/or software elements may vary in accordance with any number offactors, such as desired computational rate, power levels, heattolerances, processing cycle budget, input data rates, output datarates, memory resources, data bus speeds and other design or performanceconstraints.

In some embodiments, each of the roof modelling engine 120, the solaroptimization engine 130, the design plan engine 140 and the materialselection engine 150 may include dedicated and/or shared softwareaccessories, hardware accessories, or a combination thereof. Forexample, each of the roof modelling engine 120, the solar optimizationengine 130 and the design plan engine 140 may include a dedicatedprocessor and storage. However, in some embodiments, any combination ofthe roof modelling engine 120, the solar optimization engine 130 and thedesign plan engine 140 may share hardware resources, such as theprocessor 111 and storage 112 of the solar roof design system 100 via,e.g., a bus 113. In some embodiments, the roof modelling engine 120, thesolar optimization engine 130 and the design plan engine 140 may use anycombination of dedicated and/or shared hardware and/or softwareresources for executing via one or more processor(s) the tasks,services, functions, etc. thereof.

In some embodiments, one or more of the roof modelling engine 120, thesolar optimization engine 130 and the design plan engine 140 may beconfigured to utilize one or more exemplary AI/machine learningtechniques chosen from, but not limited to, decision trees, boosting,support-vector machines, neural networks, nearest neighbor algorithms,Naive Bayes, bagging, random forests, and the like. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, an exemplary neutral network technique may be one of, withoutlimitation, feedforward neural network, radial basis function network,recurrent neural network, convolutional network (e.g., U-net) or othersuitable network. In some embodiments and, optionally, in combination ofany embodiment described above or below, an exemplary implementation ofNeural Network may be executed as follows:

-   -   a. define Neural Network architecture/model,    -   b. transfer the input data to the exemplary neural network        model,    -   c. train the exemplary model incrementally,    -   d. determine the accuracy for a specific number of timesteps,    -   e. apply the exemplary trained model to process the        newly-received input data,    -   f. optionally and in parallel, continue to train the exemplary        trained model with a predetermined periodicity.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary trained neural network model mayspecify a neural network by at least a neural network topology, a seriesof activation functions, and connection weights. For example, thetopology of a neural network may include a configuration of nodes of theneural network and connections between such nodes. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the exemplary trained neural network model may also be specifiedto include other parameters, including but not limited to, biasvalues/functions and/or aggregation functions. For example, anactivation function of a node may be a step function, sine function,continuous or piecewise linear function, sigmoid function, hyperbolictangent function, or other type of mathematical function that representsa threshold at which the node is activated. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary aggregation function may be a mathematical function thatcombines (e.g., sum, product, etc.) input signals to the node. In someembodiments and, optionally, in combination of any embodiment describedabove or below, an output of the exemplary aggregation function may beused as input to the exemplary activation function. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the bias may be a constant value or function that may be used bythe aggregation function and/or the activation function to make the nodemore or less likely to be activated.

In some embodiments, the roof modelling engine 120 may ingest the solarroof design request 103 and create a virtual roof model by modellingeach roof plane on a roof associated with the solar roof design request103. Accordingly, in some embodiments, the solar roof design request 103may include and/or reference roof-related data for a particular roof ofa particular physical structure.

In some embodiments, the solar roof design request 103 may include aroof and/or physical structure identifier selected and/or input by auser via the user computing device 101. In some embodiments, using theroof and/or physical structure identifier, the roof modelling engine 120may access the roof-related data, e.g., in the storage 112. In someembodiments, the solar roof design request 103 may include roof-relateddata represented within the solar roof design request 103 itself asselected and/or input by a user via the user computing device 101. Insome embodiments, each data object of the roof-related data may beincluded within the solar roof design request 103, accessed in storage112 via a roof and/or physical structure identifier or provided to theroof modelling engine 120 in any other suitable way, or in anycombination thereof.

In some embodiments, the roof-related data may include one or more dataobjects representing characteristics of the roof of the particularphysical structure. In some embodiments, the roof-related data mayinclude, e.g., form-factor for the roofing accessories (e.g., asphaltshingle, tile shingle, etc.), roof compatibility with particular typesof installation mounts, one or more digital images representing visualimagery of the roof, one or more light detection and ranging (LiDAR)images representing LiDAR-based measurements of the roof, one or moreradar measurements representing radar-based measurements of the roof,location data (e.g., geospatial coordinates, address, zip code,landmark, city and/or town, region, state, territory, province, country,etc.), elevation data (e.g., based on the location data or explicitlydefined by measurement), surface topography, geographic data (e.g.,cardinal directions, structure orientation, etc.) or other informationto characterize the characteristics of a site of the roof. In someembodiments, the roof-related data may include any suitable user inputabout the roof and/or physical structure, such as, e.g., roof orproperty characteristics, measurements including length, width, height,pitch, orientation or other remeasurements or any combination thereof,or characteristics about any obstructions which may influence a solarroof design and/or bill of materials.

In some embodiments, the roof modelling engine 120 may ingest theroof-related data associated with the solar roof design request 103 tomodel each roof plane of the roof. In some embodiments, modelling eachroof plane may include determining dimensions of each roof plane to,e.g., characterize the shape of each roof plane. For example, the roofmodelling engine 120 may determine, e.g., height, width, length, pitch,among other dimensions or any combination thereof. In some embodiments,the term “roof plane” refers to an individual planar portion of a roof,where a roof may have one or more roof planes connected by edges and/orridges between adjacent roof planes.

In some embodiments, the roof modelling engine 120 may determinedimensions of each roof plane based on, e.g., measurements provided bythe roof-related data. Additionally or alternatively, the roof modellingengine 120 may implement one or more machine learning models to processimages and/or LiDAR images of the roof. In certain embodiments, the roofmodelling engine 123 may implement one or more machine learning orartificial intelligence models to process content, such as video contentof the roof, a structure, an environment in which the structure islocated, or any combination thereof. For example, a camera, vehicle,sensor, other device, or any combination thereof, may be positioned in avicinity of the structure may be utilized to capture content including,but not limited to, the video content, image content, audio content,audiovisual content, other content, or any combination thereof. Incertain embodiments, the captured content may be combined withcomputer-generated content to create augmented reality content that maybe utilized by the roof modelling engine 123 to facilitate thegenerating of a solar design for a roof. In certain embodiments, theroof modelling engine 123 may similarly utilize machine learning orartificial intelligence models to process augmented reality content thatincludes a combination of virtual reality content and actual videocontent of the roof, the structure, and/or environment.

In some embodiments, the neural networks described in the presentdisclosure may be configured to extract features from captured content.Such features may include, but are not limited to, corners, edges,regions of interest points, ridges, location information, colors,gradients, shapes, orientations, other features, or any combinationthereof. In certain embodiments, the features may be associated withobjects present in the content, an environment present in the content,anything in the content, or any combination thereof. In certainembodiments, the received content may be passed through a filter togenerate a feature map of the content, which may be utilized tofacilitate visualization of the features. In certain embodiments, thefeature map may be divided into image/content patches and may beconverted into a vector that may be processed by the neural network. Incertain embodiments, the feature map, image/content patches, vectors, orany combination thereof, may be utilized to generate localrepresentations of the content, global representations, or anycombination thereof. In certain embodiments, the representations of thecontent may be utilized to facilitate image segmentation, which mayinvolve utilizing a neural network to divide an image (or other content)into different regions based on the characteristics of pixels toidentify objects and/or boundaries to efficiently analyze the image. Incertain embodiments, the representations of the content may be utilizedfor object detection, which may involve utilizing a neural network toanalyze an image (or other content) to determine the location and theclass for each object contained within an image (or other content). Incertain embodiments, the representations of the content may be utilizedfor image classification, which may include utilizing a neural networkto extract features from an image (or other content) to classify theimage as belonging to one or more of a set of predefined categories. Incertain embodiments, the representations may be utilized forcontent-based image retrieval, which may include utilizing a neuralnetwork to search database for content having similarity and/orcorrelation to content processed by the neural network. In certainembodiments, any type of computer vision technique or function may beutilized to process the images to facilitate generation of roof planesof a roof, simulating candidate roof layouts, generating solar roofdesigns, or any combination thereof.

In some embodiments, the one or more machine learning models may includeimage segmentation, object detection, object recognition tasks,content-based image retrieval, any type of detection or recognitiontechnique, or any combination thereof. Accordingly, the one or moremachine learning models may be trained to recognize one or more roofingaccessories, materials and/or hardware or other roof features of a roofsuch as, e.g., vent stack(s), skylight(s), chimney(s), ridge vent(s),antenna(s), laminate roofing accessory(ies), roofing sheet(s), ridgecap(s), roofing frame(s), and the like or any combination thereof. Insome embodiments, the one or more machine learning models may also oralternatively be trained to recognize one or more obstructions that aresituated over the roof, such as, e.g., tree(s), power line(s), nearbystructure(s), among other objects that may obstruct a roof plane fromsunlight.

In some embodiments, the one or more machine learning models may includealgorithms for predicting spatial dimensions based on the visual/LiDARimages, such as distances between roof features on the roof and to endsof each roof plane. Accordingly, the one or more models may includeregression based algorithms for interpolating spatial data for each roofplane such as, e.g., roof plane area, roof plane length, roof planewidth, roof plane height, roof plane pitch angle, roof feature height,roof feature length, roof feature width, roof feature distance from oneor more particular roof plane edge(s), roof feature distance from a roofridge, etc., and/or for interpolating spatial data for measurements ofrelative spatial relationships between roof planes, such as, orientationangle between a first and a second roof plane, etc.

In some embodiments, the one or more machine learning models may includealgorithms for predicting spatial dimensions of obstructions based onthe visual/LiDAR images, such as size and location of one or morenon-roof objects relative to each roof plane. Accordingly, the one ormore models may include regression based algorithms for interpolatingspatial data for each obstruction relative to each roof plane such as,e.g., distance from each roof plane, obstruction height, obstructionwidth, obstruction length, obstruction area, shadowing area associatedwith a shadow cast by the obstruction on one or more roof planes, etc.

In some embodiments, the dimensions of each roof plane may be set in athree-dimensional coordinate system including each roof plane of theroof. Thus, the roof modelling engine 120 may translate the measurementsof the roof-related data, the spatial dimensions from the one or moremachine learning models, or a combination thereof, to construct athree-dimensional model of the roof including each roof plane of theroof and each obstruction. In some embodiments, the three-dimensionalmodel may include, e.g., virtual representation of measurements,locations and/or objects set in a virtual three-dimensional coordinatesystem.

In some embodiments, the roof modelling engine 120 may orient thethree-dimensional model to reflect an orientation of the roof and eachroof plane thereof. For example, the three-dimensional model mayinclude, e.g., an orientation according to cardinal directions (north,west, east, south, etc.), a geospatial location (e.g., vialatitude-longitude, address, etc.), among other location and orientationinformation.

In some embodiments, the solar optimization engine 130 may retrieve thethree-dimensional model from the roof modelling engine 120, eitherdirectly upon output by the roof modelling engine 120, or via a storedthree-dimensional model stored, e.g., in the storage 112. In someembodiments, the solar optimization engine 130 may ingest thethree-dimensional model, including the dimensions of obstructions, theroof plane dimensions, the three-dimensional model orientation, theobstruction size(s) and location(s), etc.

In some embodiments, may also access the roofing accessory database 114.In some embodiments, the roofing accessory database 114 may includespecifications for each roofing accessory part, each roofing accessorymodel, each roofing accessory type, among other forms roofing accessoryspecifications or any combination thereof. In some embodiments, theroofing accessory(ies) may include solar roofing accessories, activeroofing accessories and/or inactive roofing accessories. In someembodiments, the solar roofing accessories may include one or moresolar-based devices configured for installation on a roof, such asphotovoltaic devices, including, e.g., a solar shingle, solar panel, asolar panel attachment, among other solar-based devices configured forinstallation on a roof. In some embodiments, the active roofingaccessories may include any suitable electronic components configured tobe integrated into roofing accessories for installation on a roof, suchas integrated roofing accessories including, e.g., 5G radio integratedroofing accessories, sensor integrated roofing accessories,computing/networking device integrated roofing accessories, or any othersuitable roofing accessory having active (e.g., electronic) componentsintegrated therein or any combination thereof. In some embodiments, theinactive roofing accessories may include “conventional” roofingaccessories, such as, e.g., shingles, waterproofing membranes,underlayment, roof decking, gutters, ridge vents, ridge caps, etc.

In some embodiments, the roofing accessory database 114 may includetypes of roofing accessories, including types of solar roofingaccessories, such as, e.g., a solar shingle model, a solar shingle type,a solar panel model, a solar panel type, etc. In some embodiments, theterm “model” may refer to a manufacturer defined model for itemsconforming to a particular set of characteristics (e.g., size, use,capabilities, price, etc.). In some embodiments, the term “type” mayrefer to any grouping of similar items according to overlap incharacteristics, such as, e.g., any solar shingle having a particularform factor (e.g., matching an asphalt shingle, matching a tile shingle,constructed of a particular type of substrate or manufacturing process,etc.), including solar shingles of multiple different manufacturerdefined models.

In some embodiments, the roofing accessory database 114 may includerecords for each solar roofing accessory part type/model according to anassociated solar roofing accessory part type/model identifieridentifying each solar roofing accessory part type/model. In someembodiments, a manufacturer, retailer, contractor, or otheradministrative user may provide solar roofing accessory parts withassociated solar roofing accessory part type/model identifiers via theuser computing device 101. In some embodiments, upon entry, the user maydefine for a particular solar roofing accessory part type/model a set ofspecifications defining characteristics of the solar roofing accessorypart type/model. In some embodiments, the set of specifications mayinclude, e.g., solar roofing accessory type, solar roofing accessorymodel, dimensions, installation mounts specifying a type of connector,fastener, adapter, etc. used to install the solar roofing accessory parttype/model, as well as any solar roofing accessory part type/modelperformance characteristics. In some embodiments, the solar roofingaccessory part type/model performance characteristics may include, e.g.,expected solar generation efficiency (e.g., given a particular intensityof light), expected solar generation efficiency under one or moreconditions (e.g., cloudy weather, rain, snow, sunny conditions, shade,temperature, humidity level, etc.), expected performance deterioration,among others or any combination thereof. As a result, a record of aparticular solar roofing accessory part type/model may be entered intothe roofing accessory database 114, e.g., indexed according to the solarroofing accessory part type/model identifier. A separate record may becreated for each available solar roofing accessory part type/model tocreate an inventory of solar roofing accessory part types/modelsspecifying characteristics including performance characteristics,dimensions, capabilities, etc.

In some embodiments, the solar optimization engine 130 may perform asegmentation process to segment each roof plane of the roof according toa matching of roofing accessory specifications to locations on each roofplane in order to define solar roofing accessory part types/models thatare usable across each roof plane. In some embodiments, the solarroofing accessory types/models may be selected based on a matching ofroof plane characteristics to one or more solar roofing accessory parttypes/models according to, e.g., a form-factor for the roof (e.g.,asphalt shingle, tile shingle, etc.), roof compatibility with particulartypes of installation mounts, solar roofing accessory part type/modelsize and/or dimensions, among other characteristics or any combinationthereof. For example, based on the size of each roof plane andcompatible installation mounts and/or form-factor of roofingaccessories, the solar optimization engine 130 may identifyingconforming solar roofing accessory types/models.

Based on the conforming solar roofing accessory types/models, the solaroptimization engine 130 may produce a set of candidate roof layoutsaccording to one or more combinations that the conforming solar roofingaccessory types/models by determining various combinations of theconforming solar roofing accessory types/models that may fit within theroof plane without being cut or modified. The solar optimization engine130 may algorithmically and/or iteratively test a variety of layouts ofroofing accessory parts based on the characteristics of each roofingaccessory part to determine a set of roofing accessory parts that mayfit on each roof plane of the roof.

In some embodiments, tests of the variety of layouts may includemaximizing the area coverage on each roof plane of each combination ofthe conforming solar roofing accessory types/models. Thus, the varietyof layouts may include a quantity of individual solar roofing accessoryparts of one or more solar roofing accessory types/models that can fitwithin each roof plane. In some embodiments, one or more solar roofingaccessory types/models may be cut or otherwise divided, while others maynot. Thus, the solar optimization engine 130 may maximize the quantityof individual solar roofing accessory parts that may be placed on eachroof plane based on the dimensions of each solar roofing accessorytype/model and whether each solar roofing accessory type/model may becut to fit in an area smaller than the associated solar roofingaccessory type/model dimensions. Thus, the solar optimization engine 130may identify for each layout of the variety of layouts roof planesegments in which full sized solar roofing accessory parts may be placed(“full-device placements”) and roof plane segments in which partialsized roofing accessory parts may be placed (“partial-deviceplacements”) based on the dimensions of each conforming solar roofingaccessory type/model and whether each conforming solar roofing accessorytype/model may be divided. Based on the iteratively/algorithmicallytesting of the variety of layouts, the solar optimization engine 130 mayidentify candidate roof layouts that maximize an area of each roof planecovered by solar roofing accessories according to the solar roofingaccessory type/model characteristics and the three-dimensional model ofthe roof.

In some embodiments, the solar optimization engine 130 may incorporate anumber of active roofing accessories specified in the solar roof designrequest 103, such as one or more roofing accessories having electroniccomponents integrated therewith. Such active roofing accessories may beindivisible and thus cannot be cut down to a partial-sized device to fitin a partial sized segment of the roof. Thus, the area of one or moreroof planes may be reduced by an area of the number of active roofingaccessories. Thus, the candidate roof layouts may include one or morecombinations that include the number of the active roofing accessoriesin one or more locations across the roof plane(s) of the roof accordingto the three-dimensional model.

In some embodiments, the solar optimization engine 130 may determine anoptimal roof layout of the candidate roof layouts that optimizes solarenergy generation performance while minimizing cost and time ofconstruction. Accordingly, the solar optimization engine 130 maysimulate each candidate roof layout according to performancecharacteristics of solar roofing, solar irradiance based on a geospatiallocation and/or orientation of each roof plane, the three-dimensionalmodel including shaded areas of each roof plane due to the obstructionsas well as other suitable solar-related data. For example, the othersolar related data may include, e.g., a customer energy consumptionprofile, utility tariff information, among other suitable solar relateddata.

In some embodiments, the roofing accessory database 114 may includerecords including roofing accessory data for each solar roofingaccessory part according to an associated solar roofing accessory partidentifier identifying each solar roofing accessory part. In someembodiments, a manufacturer, retailer, contractor, or otheradministrative user may provide solar roofing accessory parts withassociated solar roofing accessory part identifiers via the usercomputing device 101. In some embodiments, upon entry, the user maydefine for roofing accessory data and for a particular solar roofingaccessory part, a set of specifications defining characteristics of thesolar roofing accessory part. In some embodiments, the set ofspecifications may include, e.g., solar roofing accessory type, solarroofing accessory model, dimensions, installation mounts specifying atype of connector, fastener, adapter, etc. used to install the solarroofing accessory part, as well as any solar roofing accessory partperformance characteristics specific to the solar roofing accessorypart. In some embodiments, the solar roofing accessory part performancecharacteristics of the roofing accessory data may include, e.g.,measured solar generation efficiency (e.g., given a particular intensityof light), measured solar generation efficiency under one or moreconditions (e.g., cloudy weather, rain, snow, sunny conditions, shade,temperature, humidity level, etc.), measured quantity of defects,measured and/or expected performance deterioration, among others or anycombination thereof. As a result, a record of a particular solar roofingaccessory part may be entered into the roofing accessory database 114,e.g., indexed according to the solar roofing accessory part identifier.A separate record may be created for each available solar roofingaccessory part to create an inventory of solar roofing accessory partsspecifying characteristics including performance characteristics,dimensions, capabilities, etc.

In some embodiments, the roofing accessory database 114 may also includerecords for other active and inactive roofing accessories indexedaccording to roofing accessory part identifiers. In some embodiments,each record of the roofing accessories may include a set ofspecifications including, e.g., roofing accessory type, roofingaccessory model, dimensions, installation mounts specifying a type ofconnector, fastener, adapter, etc. used to install the roofing accessorypart, as well as any roofing accessory part performance characteristicsspecific to each roofing accessory part, including whether each roofingaccessory part is active or inactive, whether each roofing accessory maybe cut or modified, among other roofing accessory part performancecharacteristics.

Accordingly, in some embodiments, the solar optimization engine 130 maysimulate solar irradiance of each roof plane, shading of each roofplane, and solar energy generation according to the candidate rooflayouts by simulating each candidate roof layout with the performancecharacteristics of solar roofing accessory parts. In some embodiments,to reduce computational resources, the candidate roof layouts may besimulated with expected measures of the performance characteristicsbased on the solar roofing accessory types/models of each candidate rooflayout rather than simulating each candidate roof layout with eachavailable solar roofing accessory part. In some embodiments, the solaroptimization engine 130 may simulate each candidate roof layout using ahighest performing set of solar roofing accessory parts based on theassociated performance characteristics of each solar roofing accessorypart. Thus, the simulation may include sequentially locating each solarroofing accessory part into a location in a candidate roof layout, thesequence being defined by a descending order of the performancecharacteristics of each solar roofing accessory. In some embodiments,the solar optimization engine 130 may simulate every possiblecombination of solar roofing accessory parts in each candidate rooflayout.

In some embodiments, the solar optimization engine 130 may assign toeach candidate roof layout a utilization prediction based on thesimulation(s) associated each candidate roof layout. In someembodiments, utilization prediction may include, e.g., a utilizationscore of each candidate roof layout based on, e.g., a predictedinstallation metric associated with the candidate roof layout (e.g.,according to learned parameters learned from a historical of solarroofing accessory part(s) and associated installation metric(s)), thesolar roofing accessory part performance characteristic associated witheach solar roofing accessory part, and the simulated solar irradiance,shading, etc. In some embodiments, the utilization prediction mayinclude, e.g., a predicted utilization score for each solar roofingaccessory part and/or each solar roofing accessory type/model specifiedin each candidate roof layout based on installation mount, performancecharacteristics, simulated solar irradiance, simulated shading,dimensions, among other features affecting the performance ofinstallation and solar generation associated with each solar roofingaccessory part and/or each solar roofing accessory type/model. In someembodiments, the solar optimization engine 130 may utilize one or moremachine learning models to ingest the characteristics of each solarroofing accessory part and/or each solar roofing accessory type/model ofeach candidate roof layout, the three-dimensional model, and theroofing-related data.

In some embodiments, the solar optimization engine 130 may output aparticular roof layout of the candidate roof layouts based on acandidate roof layout having a greatest utilization prediction. In someembodiments, the particular roof layout may define the location on eachroof plane of each solar roofing accessory part and each active/inactiveroofing accessory (“non-solar roofing accessory parts”). The particularlayout may also be defined for each solar roofing accessory part in theparticular layout the utilization score according to the location oneach roof plane of each solar roofing accessory part.

In some embodiments, the design plan engine 140 may ingest theparticular layout and access, e.g., in the storage 112, data indicativeof material cost of each solar roofing accessory part and each non-solarroofing accessory part, equipment associated with installation of eachsolar roofing accessory part and each non-solar roofing accessory part,labor costs associated with installation of each solar roofing accessorypart and each non-solar roofing accessory part, installation timeassociated with each solar roofing accessory part and each non-solarroofing accessory part, among other installation related data or anycombination thereof to complete a weatherproof solar roof system.

In some embodiments, based on the installation related data for theparticular layout, the design plan engine 140 may determine a solar roofdesign that includes a list of the solar roofing accessory partidentifiers identifying each solar roofing accessory part of theparticular layout, as well as equipment, materials, labor time, laborcost, and installation costs associated with constructing the solar roofsystem on the roof. In some embodiments, the design plan engine 140 maystore the solar roof design in the storage 112 using the solar roofingaccessory part identifiers to reference the record of each solar roofingaccessory part used in the particular layout of the solar roof design.

As a result, the solar roof design may be used to track a lifetime ofeach solar roofing accessory part used in the solar roof design. Forexample, a user, such as the construction entity that installed theparticular layout, the manufacturer, the owner of the structureassociated with the roof, or other user, may provide updates on eachsolar roofing accessory part. For example, the user may use the usercomputing device 101 to modify the record of each solar roofingaccessory part in the roofing accessory database 114 based on the solarroof design stored in the storage 112 in order to update each recordwith an indication of installation, any performance deterioration,whether each solar roofing accessory part was cut or otherwise divided,any defects, solar generation performance, among other attributesthroughout the lifetime of each solar roofing accessory part.Accordingly, the solar roof design and the roofing accessory database114 may be employed to track each solar roofing accessory part to enablea user to ascertain if and when a particular solar roofing accessorypart needs replacing, as well as for training machine learning modelsand/or aggregating statistics regarding performance and failure amongother lifetime data of each solar roofing accessory part.

In some embodiments, the material selection engine 150 may access thesolar roof design to generate a list of materials, components and/ortools for constructing the solar roof design on the roof. In someembodiments, the list of materials, may include, e.g., a type offastener for securing each roofing accessory part including each solarroofing accessory part to the roof, a number of fasteners needed tosecure each roofing accessory part including each solar roofingaccessory part to the roof, brackets, adhesives, frames, raw materials(e.g., lumber, brick, stone, cement, asphalt, etc.), wiring,waterproofing, hammer, hammer type, screw driver, screw driver type,drill, drill bits, nail gun, staple gun, or any other material,component and/or tools or any combination thereof.

In some embodiments, the material selection engine 150 may include,e.g., one or more machine learning models for learning one or morematerials and/or tools associated with each solar roofing accessory partand/or other roofing accessory part of the solar roof design. Thus, foreach roofing accessory part in the solar roof design, the materialselection engine 150 may identify the associated materials/componentstype(s) and a quantity thereof for installing each roofing accessorypart. In some embodiments, for some roofing accessory parts, such asshingles, the materials/components may be learned based on featuresincluding an area of coverage of the roofing accessory parts, a numberof the roofing accessory parts in the solar roof design, a locationand/or orientation of the virtual roof model, or other feature or anycombination thereof. Similarly, in some embodiments, for solar roofingaccessory parts and/or any other electronic roofing accessory parts, thematerials/components may be learned based on features include a numberof the solar roofing accessory parts and/or electronic roofing accessoryparts, an area of coverage of the solar roofing accessory parts, alocation and/or orientation of the virtual roof model, or other featureor any combination thereof. In some embodiments, the one or more machinelearning models may be trained based on a roof type, such as, e.g., aresidential roof, a commercial roof, a roof with wood decking, a roofwith non-wood decking, or other type of roof or any combination thereof.

In some embodiments, the material selection engine 150 may include,e.g., a library of material rules and tool rules. In some embodiments,the material rules may include a mapping of a type and an amount of oneor more materials and/or components for each solar roofing accessorypart or other roofing accessory part. Thus, for each roofing accessorypart in the solar roof design, the material selection engine 150 mayidentify the associated materials/components type(s) and a quantitythereof for installing each roofing accessory part. In some embodiments,for some roofing accessory parts, such as shingles, thematerials/components may be mapped based on an area of coverage of theroofing accessory parts or on a number of the roofing accessory parts inthe solar roof design. In some embodiments, for solar roofing accessoryparts and/or any other electronic roofing accessory parts, thematerials/components may be mapped based on a number of the solarroofing accessory parts and/or electronic roofing accessory parts. Insome embodiments, the material rules may be based on a roof type, suchas, e.g., a residential roof, a commercial roof, a roof with wooddecking, a roof with non-wood decking, or other type of roof or anycombination thereof.

In some embodiments, the tool rules may include a mapping of a tool ortool type for each solar roofing accessory part, other roofing accessorypart and/or material. For example, specific fasteners may be mapped toparticular tools (e.g., nails mapped to hammer, Philips head screwmapped to Philips head screw driver, asphalt shingle mapped to nail gunand/or staple gun, etc.). In another example, particular solar roofingaccessory parts may be mapped to particular adhesives and/or tools(e.g., mallet, hex wrench, clamp, etc.) for installing the particularsolar roofing accessory part. Accordingly, the tool rules may identify alist of tools needed to complete installation of the solar roof designbased on the solar roofing accessories, the other roofing accessoriesand/or the associated materials/components produced by the materialrules. In some embodiments, the material selection engine 150 mayidentify a number of each tool based on, e.g., a number of workers(e.g., input by a user via the user computing device 101), apredetermined number of workers, a roof area, a number of solar roofingaccessory parts, a construction timeline (e.g., input by a user via theuser computing device 101), among other characteristics of theconstruction of the solar roof design.

In some embodiments, the material selection engine 150 may correlate aconstruction timeline and a roof area to a number of workers needed forconstruction based on an algorithm correlating a construction time perunit area per worker for a roof construction project. Based on thenumber of workers, the material selection engine 150 may determine thenumber of tools to maximize worker production and minimize constructiontime. In some embodiments, the material selection engine 150 may utilizeone or more machine learning models to correlate each worker to aconstruction time and/or construction area over a time period or othermetric of productivity. In some embodiments, the correlation may bebased on features such as, e.g., solar roofing accessory part to beinstalled, tool being used, materials needed, time of year, among otherfeatures or any combination thereof. Thus, a time to install eachroofing accessory part in the solar roof design can be determined, andthe total construction timeline can be minimized according to the timeto install each roofing accessory part and maximum and/or minimumnumbers of workers available.

In some embodiments, the material list and tool list may be added to thesolar roof design. In some embodiments, the solar roof design 105 may beoutput to the user computing device 101 to present the solar roof design105 to a user such as, e.g., a contractor, installer, manufacture,property owner or other user or any combination thereof. Thus, in someembodiments, the solar roof design system 100 may produce a design for asolar roof system to enable a user to plan for and carry outinstallation with accurate and optimized layouts of solar roofingaccessory parts and the associated costs, time and equipment. Thus, thesolar roof design 105 may provide complete and comprehensive bill ofmaterials for the entire roofing system integrating solar and non-solarroofing accessories, a design guide based on optimization of locationand count of solar roofing accessory parts and non-solar roofingaccessories, providing contractors with multiple solar shingle designsto meet a variety of customer goals, including solar production, cost,aesthetics, among other end uses or any combination thereof.

FIG. 2 is a block diagram of an exemplary computer-based system forsolar roof design using automated structural modelling in accordancewith one or more embodiments of the present disclosure.

In some embodiments, the roof modelling engine 120 may utilize a roofspace model 220 to ingest input features of the roof-related data of thesolar roof design request 103. In some embodiments, input features ofthe roof-related data may include imagery such as imagery taken fromeither aerial or ground locations. The imagery may show the home, theproperty extents, any structures, trees, obstructions located on theproperty, and any homes, structures, trees, or obstructions located onadjacent properties within a radius of the home. The input features alsoinclude when available, LiDAR data, elevation data, surface topography,geospatial data, structure, roof and/or roof plane orientations withrespect to cardinal directions and/or movements of the sun, or otherinformation to help determine the characteristics of the site or anycombination thereof. The inputs also include any user input about theproperty, such as roof or property characteristics, or characteristicsabout any obstructions which may influence the design or bill ofmaterials.

In some embodiments, the roof space model 220 may ingest the solar roofdesign request 103 and create a virtual roof model include a virtualmodel of each roof plane on a roof associated with the solar roof designrequest 103. In some embodiments, the roof space model 220 may includealgorithms for predicting spatial dimensions based on the visual/LiDARimages, such as distances between roof features on the roof and to endsof each roof plane. Accordingly, the one or more models may includeregression based algorithms for interpolating spatial data for each roofplane such as, e.g., roof plane area, roof plane length, roof planewidth, roof plane height, roof plane pitch angle, roof feature height,roof feature length, roof feature width, roof feature distance from oneor more particular roof plane edge(s), roof feature distance from a roofridge, etc., and/or for interpolating spatial data for measurements ofrelative spatial relationships between roof planes, such as, orientationangle between a first and a second roof plane, etc.

In some embodiments, the roof space model 220 processes the featurevector with parameters to produces a prediction of spatial data of eachroof plane of the roof. In some embodiments, the parameters of the roofspace model 220 may be implemented in a suitable machine learning modelincluding a prediction machine learning model, such as, e.g., LinearRegression, Logistic Regression, Ridge Regression, Lasso Regression,Polynomial Regression, Bayesian Linear Regression (e.g., Naive Bayesregression), a convolutional neural network (CNN), a recurrent neuralnetwork (RNN), decision trees, random forest, support vector machine(SVM), K-Nearest Neighbors, or any other suitable algorithm forpredicting output values based on input values. In some embodiments, forcomputational efficiency while preserving accuracy of predictions, theroof space model 220 may advantageously include a random forest model.

In some embodiments, the roof space model 220 processes the featuresencoded in the feature vector by applying the parameters of theprediction machine learning model to produce a model output vector. Insome embodiments, the model output vector may be decoded to generate oneor more numerical output values indicative of spatial data of each roofplane of the roof. In some embodiments, the model output vector mayinclude or may be decoded to reveal the output value(s) based on amodelled correlation between the feature vector and a target output. Insome embodiments, the numerical output may represent spatial data ofeach roof plane of the roof including, e.g., a three-dimensional virtualmodel of the roof.

In some embodiments, the parameters of the roof space model 220 may betrained based on known outputs. For example, the roof-related data maybe paired with a target value or known value to form a training pair,such as a historical roof-related data and an observed result and/orhuman annotated value representing a data point in the relationshipbetween the historical roof-related data and spatial data of each roofplane. In some embodiments, the roof-related data may be provided to theroof space model 220, e.g., encoded in a feature vector, to produce apredicted output value. In some embodiments, an optimizer 222 associatedwith the roof space model 220 may then compare the predicted outputvalue with the known output of a training pair including the historicalroof-related data to determine an error of the predicted output value.In some embodiments, the optimizer 222 may employ a loss function, suchas, e.g., Hinge Loss, Multi-class SVM Loss, Cross Entropy Loss, NegativeLog Likelihood, or other suitable classification loss function todetermine the error of the predicted output value based on the knownoutput.

In some embodiments, the known output may be obtained after the roofspace model 220 produces the prediction, such as in online learningscenarios. In such a scenario, the roof space model 220 may receive theroof-related data and generate the model output vector to produce anoutput value representing spatial data of each roof plane of the roofincluding, e.g., a three-dimensional virtual model of the roof.Subsequently, a user may provide roof model feedback 228 by, e.g.,modifying, adjusting, removing, and/or verifying the output value via asuitable roof model feedback 228 mechanism, such as a user interfacedevice (e.g., keyboard, mouse, touch screen, user interface, or otherinterface mechanism of a user device or any suitable combinationthereof). The roof model feedback 228 may be paired with theroof-related data to form the training pair and the optimizer 222 maydetermine an error of the predicted output value using the roof modelfeedback 228.

In some embodiments, based on the error, the optimizer 222 may updatethe parameters of the roof space model 220 using a suitable trainingalgorithm such as, e.g., backpropagation for a prediction machinelearning model. In some embodiments, backpropagation may include anysuitable minimization algorithm such as a gradient method of the lossfunction with respect to the weights of the prediction machine learningmodel. Examples of suitable gradient methods include, e.g., stochasticgradient descent, batch gradient descent, mini-batch gradient descent,or other suitable gradient descent technique. As a result, the optimizer222 may update the parameters of the roof space model 220 based on theerror of predicted labels in order to train the roof space model 220 tomodel the correlation between roof-related data and spatial data of eachroof plane of the roof in order to produce more accurate output valuesbased on roof-related data.

In some embodiments, the dimensions of each roof plane may be set in athree-dimensional coordinate system including each roof plane of theroof. Thus, the roof modelling engine 120 may translate the measurementsof the roof-related data, the spatial dimensions from the one or moremachine learning models, or a combination thereof, to construct athree-dimensional model of the roof including each roof plane of theroof and each obstruction. In some embodiments, the three-dimensionalmodel may include, e.g., virtual representation of measurements,locations and/or objects set in a virtual three-dimensional coordinatesystem.

In some embodiments, the roof modelling engine 120 may orient thethree-dimensional model to reflect an orientation of the roof and eachroof plane thereof. For example, the three-dimensional model mayinclude, e.g., an orientation according to cardinal directions (north,west, east, south, etc.), a geospatial location (e.g., vialatitude-longitude, address, etc.), among other location and orientationinformation.

FIG. 3 is a block diagram of an exemplary computer-based system forsolar roof design using structure-specific and accessory-specificmodelling and analysis based on per-accessory tracking data inaccordance with one or more embodiments of the present disclosure.

In some embodiments, the solar optimization engine 130 may retrieve thethree-dimensional model 224 from the roof modelling engine 120, eitherdirectly upon output by the roof modelling engine 120, or via a storedthree-dimensional model 224 stored, e.g., in the storage 112. In someembodiments, a roof segmentation model 330 of the solar optimizationengine 130 may ingest the three-dimensional model 224, including thedimensions of obstructions, the roof plane dimensions, thethree-dimensional model orientation, the obstruction size(s) andlocation(s), etc. In some embodiments, the roof segmentation model 330may also ingest roofing accessory characteristics 131 of each solarroofing accessory type/model as well as one or more non-solar (activeand/or inactive) roofing accessories. In some embodiments, the roofsegmentation model 330 of may segment each roof plane of the roofaccording to candidate locations on each roof plane of both solar andnon-solar roofing accessories based on the roofing accessorycharacteristics 131 and the three-dimensional model 224.

In some embodiments, the roof segmentation model 330 processes theroofing accessory characteristics 131 and the three-dimensional model224 with parameters to produces a prediction of candidate roof layouts331. In some embodiments, the parameters of the roof segmentation model330 may be implemented in a suitable machine learning model including aprediction machine learning model, such as, e.g., Linear Regression,Logistic Regression, Ridge Regression, Lasso Regression, PolynomialRegression, Bayesian Linear Regression (e.g., Naive Bayes regression), aconvolutional neural network (CNN), a recurrent neural network (RNN),decision trees, random forest, support vector machine (SVM), K-NearestNeighbors, or any other suitable algorithm for predicting output valuesbased on input values. In some embodiments, for computational efficiencywhile preserving accuracy of predictions, the roof segmentation model330 may advantageously include a random forest model.

In some embodiments, the roof segmentation model 330 processes thefeatures encoded in the roofing accessory characteristics 131 and thethree-dimensional model 224 by applying the parameters of the predictionmachine learning model to produce a model output vector. In someembodiments, the model output vector may be decoded to generate one ormore numerical output values indicative of candidate roof layouts 331.In some embodiments, the model output vector may include or may bedecoded to reveal the output value(s) based on a modelled correlationbetween the roofing accessory characteristics 131 and thethree-dimensional model 224 and a target output. In some embodiments,the numerical output may represent candidate roof layouts 331.

In some embodiments, the parameters of the roof segmentation model 330may be trained based on known outputs. For example, the roofingaccessory characteristics 131 and the three-dimensional model 224 may bepaired with a target value or known value to form a training pair, suchas a historical roofing accessory characteristics 131 and thethree-dimensional model 224 and an observed result and/or humanannotated value representing a data point in the relationship betweenthe historical roofing accessory characteristics 131 and thethree-dimensional model 224 and candidate roof layouts 331. In someembodiments, the roofing accessory characteristics 131 and thethree-dimensional model 224 may be provided to the roof segmentationmodel 330, e.g., encoded in a roofing accessory characteristics 131 andthe three-dimensional model 224, to produce a predicted output value. Insome embodiments, an optimizer 332 associated with the roof segmentationmodel 330 may then compare the predicted output value with the knownoutput of a training pair including the historical roofing accessorycharacteristics 131 and the three-dimensional model 224 to determine anerror of the predicted output value. In some embodiments, the optimizer332 may employ a loss function, such as, e.g., Hinge Loss, Multi-classSVM Loss, Cross Entropy Loss, Negative Log Likelihood, or other suitableclassification loss function to determine the error of the predictedoutput value based on the known output.

In some embodiments, tests of the variety of layouts may includemaximizing the area coverage on each roof plane of each combination ofthe conforming solar roofing accessory types/models. Thus, the varietyof layouts may include a quantity of individual solar roofing accessoryparts of one or more solar roofing accessory types/models that can fitwithin each roof plane. In some embodiments, one or more solar roofingaccessory types/models may be cut or otherwise divided, while others maynot. Thus, the roof segmentation model 330 may maximize the quantity ofindividual solar roofing accessory parts that may be placed on each roofplane based on the dimensions of each solar roofing accessory type/modeland whether each solar roofing accessory type/model may be cut to fit inan area smaller than the associated solar roofing accessory type/modeldimensions. Thus, the roof segmentation model 330 may identify for eachlayout of the variety of layouts roof plane segments in which full sizedsolar roofing accessory parts may be placed (“full-device placements”)and roof plane segments in which partial sized roofing accessory partsmay be placed (“partial-sized placements”) based on the dimensions ofeach conforming solar roofing accessory type/model and whether eachconforming solar roofing accessory type/model may be divided.

In some embodiments, the roof segmentation model 330 may incorporate anumber of active roofing accessories specified in the solar roof designrequest 103, such as one or more roofing accessories having electroniccomponents integrated therewith. Such active roofing accessories may bedivisible or indivisible. In some embodiments, active roofingaccessories that are divisible may be cut or otherwise divided to form apartial sized active roofing accessory from a full-sized active roofingaccessory in order to file the partial sized active roofing accessorywithin the partial sized segment of the roof. In some embodiments,active roofing accessories that are indivisible cannot be cut to fit ina partial sized segment of the roof. Thus, the area of one or more roofplanes may be reduced by an area of the number of active roofingaccessories. Thus, the candidate roof layouts may include one or morecombinations that include the number of the active roofing accessoriesin one or more locations across the roof plane(s) of the roof accordingto the three-dimensional model.

In some embodiments, the solar optimization engine 130 may determine anoptimal roof layout of the candidate roof layouts 331 that optimizessolar energy generation performance while minimizing cost and time ofconstruction. Accordingly, a solar optimization model 334 of the solaroptimization engine 130 may simulate each candidate roof layout 331according to performance characteristics of solar roofing, solarirradiance based on a geospatial location and/or orientation of eachroof plane, the three-dimensional model including shaded areas of eachroof plane due to the obstructions as well as other suitablesolar-related data. For example, the other solar related data mayinclude, e.g., a customer energy consumption profile, utility tariffinformation, among other suitable solar related data. Accordingly, insome embodiments, the solar optimization model 334 may simulate solarirradiance of each roof plane, shading of each roof plane, and solarenergy generation according to the candidate roof layouts 331 bysimulating each candidate roof layout 331 with the roofing accessorycharacteristics 131 of solar roofing accessory parts.

In some embodiments, the solar optimization model 334 may produce aprediction of a solar score for each solar roofing accessory part ofeach candidate roof layout 331 to thereby predict a utilizationprediction 335 for each candidate roof layout 331. In some embodiments,the parameters of the solar optimization model 334 may be implemented ina suitable machine learning model including a prediction machinelearning model, such as, e.g., Linear Regression, Logistic Regression,Ridge Regression, Lasso Regression, Polynomial Regression, BayesianLinear Regression (e.g., Naive Bayes regression), a convolutional neuralnetwork (CNN), a recurrent neural network (RNN), decision trees, randomforest, support vector machine (SVM), K-Nearest Neighbors, or any othersuitable algorithm for predicting output values based on input values.In some embodiments, for computational efficiency while preservingaccuracy of predictions, the solar optimization model 334 mayadvantageously include a random forest model.

In some embodiments, the solar optimization model 334 processes thefeatures encoded in the feature vector by applying the parameters of theprediction machine learning model to produce a model output vector. Insome embodiments, the model output vector may be decoded to generate oneor more numerical output values indicative of the score of each solarroofing accessory part and/or the utilization prediction of eachcandidate roof layout 331. In some embodiments, the model output vectormay include or may be decoded to reveal the output value(s) based on amodelled correlation between the feature vector and a target output. Insome embodiments, the numerical output may represent the score of eachsolar roofing accessory part and/or the utilization prediction of eachcandidate roof layout 331.

In some embodiments, the parameters of the solar optimization model 334may be trained based on known outputs. For example, the candidate rooflayout 331 may be paired with a target value or known value to form atraining pair, such as a historical candidate roof layout 331 and anobserved result and/or human annotated value representing a data pointin the relationship between the historical candidate roof layout 331 anda known score of each solar roofing accessory part and/or utilizationprediction of each candidate roof layout 331. In some embodiments, thecandidate roof layout 331 may be provided to the solar optimizationmodel 334, e.g., encoded in a feature vector, to produce a predictedoutput value. In some embodiments, an optimizer 336 associated with thesolar optimization model 334 may then compare the predicted output valuewith the known output of a training pair including the historicalcandidate roof layout 331 to determine an error of the predicted outputvalue. In some embodiments, the optimizer 336 may employ a lossfunction, such as, e.g., Hinge Loss, Multi-class SVM Loss, Cross EntropyLoss, Negative Log Likelihood, or other suitable classification lossfunction to determine the error of the predicted output value based onthe known output.

In some embodiments, the known output may be obtained after the solaroptimization model 334 produces the prediction, such as in onlinelearning scenarios. In such a scenario, the solar optimization model 334may receive the candidate roof layouts 331 and generate the model outputvector to produce an output value representing the score of each solarroofing accessory part and/or the utilization prediction of eachcandidate roof layout 331. Subsequently, a user may provide feedback 338by, e.g., modifying, adjusting, removing, and/or verifying the outputvalue via a suitable feedback 338 mechanism, such as a user interfacedevice (e.g., keyboard, mouse, touch screen, user interface, or otherinterface mechanism of a user device or any suitable combinationthereof). The feedback 338 may be paired with the candidate roof layouts331 to form the training pair and the optimizer 336 may determine anerror of the predicted output value using the feedback 338.

In some embodiments, based on the error, the optimizer 336 may updatethe parameters of the solar optimization model 334 using a suitabletraining algorithm such as, e.g., backpropagation for a predictionmachine learning model. In some embodiments, backpropagation may includeany suitable minimization algorithm such as a gradient method of theloss function with respect to the weights of the prediction machinelearning model. Examples of suitable gradient methods include, e.g.,stochastic gradient descent, batch gradient descent, mini-batch gradientdescent, or other suitable gradient descent technique. As a result, theoptimizer 336 may update the parameters of the solar optimization model334 based on the error of predicted labels in order to train the solaroptimization model 334 to model the correlation between candidate rooflayouts 331 and the score of each solar roofing accessory part and/orthe utilization prediction of each candidate roof layout 331 in order toproduce more accurate output values based on candidate roof layout 331.

In some embodiments, the known output for the roof segmentation model330 may similarly be obtained after the roof segmentation model 330and/or the solar optimization model 334 produces the prediction, such asin online learning scenarios. In such a scenario, the roof segmentationmodel 330 may receive the roofing accessory characteristics 131 and thethree-dimensional model 224 and generate the model output vector toproduce output values representing candidate roof layouts 331.Subsequently, a user may provide feedback 338 by, e.g., modifying,adjusting, removing, and/or verifying the output value via a suitablefeedback 338 mechanism, such as a user interface device (e.g., keyboard,mouse, touch screen, user interface, or other interface mechanism of auser device or any suitable combination thereof). The feedback 338 maybe paired with the roofing accessory characteristics 131 and thethree-dimensional model 224 to form the training pair and the optimizer332 may determine an error of the predicted output value using thefeedback 338.

In some embodiments, based on the error, the optimizer 332 may updatethe parameters of the roof segmentation model 330 using a suitabletraining algorithm such as, e.g., backpropagation for a predictionmachine learning model. In some embodiments, backpropagation may includeany suitable minimization algorithm such as a gradient method of theloss function with respect to the weights of the prediction machinelearning model. Examples of suitable gradient methods include, e.g.,stochastic gradient descent, batch gradient descent, mini-batch gradientdescent, or other suitable gradient descent technique. As a result, theoptimizer 332 may update the parameters of the roof segmentation model330 based on the error of predicted labels in order to train the roofsegmentation model 330 to model the correlation between roofingaccessory characteristics 131 and the three-dimensional model 224 andcandidate roof layouts 331 in order to produce more accurate outputvalues based on roofing accessory characteristics 131 and thethree-dimensional model 224.

FIG. 4 is a block diagram of an exemplary computer-based system forsolar roof design including automated design plan prediction inaccordance with one or more embodiments of the present disclosure.

In some embodiments, the design plan engine 140 may ingest a particularlayout associated with the candidate roof layout having the highestutilization prediction 335 predicted by the solar optimization model334. In some embodiments, the design plan engine 140 may utilize adesign plan model 440 to automatically ingest the particular layout and,e.g., solar roofing system construction data 141 such as, e.g., thematerial cost of each solar roofing accessory part and each non-solarroofing accessory part, the equipment associated with installation ofeach solar roofing accessory part and each non-solar roofing accessorypart, the labor costs associated with installation of each solar roofingaccessory part and each non-solar roofing accessory part, theinstallation time associated with each solar roofing accessory part andeach non-solar roofing accessory part, among other solar roofing systemconstruction data 141 or any combination thereof to complete aweatherproof solar roof system.

In some embodiments, the design plan model 440 processes the featurevector with parameters to produces a prediction of solar roof design105. In some embodiments, the parameters of the design plan model 440may be implemented in a suitable machine learning model including aprediction machine learning model, such as, e.g., Linear Regression,Logistic Regression, Ridge Regression, Lasso Regression, PolynomialRegression, Bayesian Linear Regression (e.g., Naive Bayes regression), aconvolutional neural network (CNN), a recurrent neural network (RNN),decision trees, random forest, support vector machine (SVM), K-NearestNeighbors, or any other suitable algorithm for predicting output valuesbased on input values. In some embodiments, for computational efficiencywhile preserving accuracy of predictions, the design plan model 440 mayadvantageously include a random forest model.

In some embodiments, the design plan model 440 processes the featuresencoded in the feature vector by applying the parameters of theprediction machine learning model to produce a model output vector. Insome embodiments, the model output vector may be decoded to generate oneor more numerical output values indicative of solar roof design 105. Insome embodiments, the model output vector may include or may be decodedto reveal the output value(s) based on a modelled correlation betweenthe feature vector and a target output. In some embodiments, thenumerical output may represent a solar roof design 105 including acomprehensive whole roofing system including location of active andinactive solar shingles, a design optimized to balance installation costagainst production value and customer energy needs, and a full BOM forall roofing and solar related components.

In some embodiments, the parameters of the design plan model 440 may betrained based on known outputs. For example, the particular layout andthe solar roofing system construction data 141 may be paired with atarget value or known value to form a training pair, such as ahistorical the particular layout and the solar roofing systemconstruction data 141 and an observed result and/or human annotatedvalue representing a data point in the relationship between thehistorical the particular layout and the solar roofing systemconstruction data 141 and the solar roof design 105. In someembodiments, the particular layout and the solar roofing systemconstruction data 141 may be provided to the design plan model 440,e.g., encoded in a feature vector, to produce a predicted output value.In some embodiments, an optimizer 442 associated with the design planmodel 440 may then compare the predicted output value with the knownoutput of a training pair including the historical the particular layoutand the solar roofing system construction data 141 to determine an errorof the predicted output value. In some embodiments, the optimizer 442may employ a loss function, such as, e.g., Hinge Loss, Multi-class SVMLoss, Cross Entropy Loss, Negative Log Likelihood, or other suitableclassification loss function to determine the error of the predictedoutput value based on the known output.

In some embodiments, the known output may be obtained after the designplan model 440 produces the prediction, such as in online learningscenarios. In such a scenario, the design plan model 440 may receive theparticular layout and the solar roofing system construction data 141 andgenerate the model output vector to produce an output value representingthe solar roof design 105. Subsequently, a user may provide feedback 448by, e.g., modifying, adjusting, removing, and/or verifying the outputvalue via a suitable feedback 448 mechanism, such as a user interfacedevice (e.g., keyboard, mouse, touch screen, user interface, or otherinterface mechanism of a user device or any suitable combinationthereof). The feedback 448 may be paired with the particular layout andthe solar roofing system construction data 141 to form the training pairand the optimizer 442 may determine an error of the predicted outputvalue using the feedback 448.

In some embodiments, based on the error, the optimizer 442 may updatethe parameters of the design plan model 440 using a suitable trainingalgorithm such as, e.g., backpropagation for a prediction machinelearning model. In some embodiments, backpropagation may include anysuitable minimization algorithm such as a gradient method of the lossfunction with respect to the weights of the prediction machine learningmodel. Examples of suitable gradient methods include, e.g., stochasticgradient descent, batch gradient descent, mini-batch gradient descent,or other suitable gradient descent technique. As a result, the optimizer442 may update the parameters of the design plan model 440 based on theerror of predicted labels in order to train the design plan model 440 tomodel the correlation between the particular layout and the solarroofing system construction data 141 and solar roof design 105 in orderto produce more accurate output values based on the particular layoutand the solar roofing system construction data 141.

In some embodiments, the solar roof design 105 may be output to the usercomputing device 101 to present the solar roof design 105 to a user suchas, e.g., a contractor, installer, manufacture, property owner or otheruser or any combination thereof. Thus, the solar roof design system 100may produce a design for a solar roof system to enable a user to planfor and carry out installation with accurate and optimized layouts ofsolar roofing accessory parts and the associated costs, time andequipment. Thus, the solar roof design 105 may provide complete andcomprehensive bill of materials for the entire roofing systemintegrating solar and non-solar roofing accessories, a design guidebased on optimization of location and count of solar roofing accessoryparts and non-solar roofing accessories, providing contractors withmultiple solar shingle designs to meet a variety of customer goals,including solar production, cost, aesthetics, among other end uses orany combination thereof.

FIG. 5 depicts a block diagram of an exemplary computer-based system andplatform 500 in accordance with one or more embodiments of the presentdisclosure. However, not all of these accessories may be required topractice one or more embodiments, and variations in the arrangement andtype of the accessories may be made without departing from the spirit orscope of various embodiments of the present disclosure. In someembodiments, the illustrative computing devices and the illustrativecomputing accessories of the exemplary computer-based system andplatform 500 may be configured to manage a large number of members andconcurrent transactions, as detailed herein. In some embodiments, theexemplary computer-based system and platform 500 may be based on ascalable computer and network architecture that incorporates variesstrategies for assessing the data, caching, searching, and/or databaseconnection pooling. An example of the scalable architecture is anarchitecture that is capable of operating multiple servers.

In some embodiments, referring to FIG. 5 , member computing device 502,member computing device 503 through member computing device 504 (e.g.,clients) of the exemplary computer-based system and platform 500 mayinclude virtually any computing device capable of receiving and sendinga message over a network (e.g., cloud network), such as network 505, toand from another computing device, such as servers 506 and 507, eachother, and the like. In some embodiments, the member devices 502-504 maybe personal computers, multiprocessor systems, microprocessor-based orprogrammable consumer electronics, network PCs, and the like. In someembodiments, one or more member devices within member devices 502-504may include computing devices that typically connect using a wirelesscommunications medium such as cell phones, smart phones, pagers, walkietalkies, radio frequency (RF) devices, infrared (IR) devices, citizensband radio, integrated devices combining one or more of the precedingdevices, or virtually any mobile computing device, and the like. In someembodiments, one or more member devices within member devices 502-504may be devices that are capable of connecting using a wired or wirelesscommunication medium such as a PDA, POCKET PC, wearable computer, alaptop, tablet, desktop computer, a netbook, a video game device, apager, a smart phone, an ultra-mobile personal computer (UMPC), and/orany other device that is equipped to communicate over a wired and/orwireless communication medium (e.g., NFC, RFID, NBIOT, 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, OFDM, OFDMA, LTE, satellite, ZigBee, etc.). Insome embodiments, one or more member devices within member devices502-504 may include may run one or more applications, such as Internetbrowsers, mobile applications, voice calls, video games,videoconferencing, and email, among others. In some embodiments, one ormore member devices within member devices 502-504 may be configured toreceive and to send web pages, and the like. In some embodiments, anexemplary specifically programmed browser application of the presentdisclosure may be configured to receive and display graphics, text,multimedia, and the like, employing virtually any web based language,including, but not limited to Standard Generalized Markup Language(SMGL), such as HyperText Markup Language (HTML), a wireless applicationprotocol (WAP), a Handheld Device Markup Language (HDML), such asWireless Markup Language (WML), WMLScript, XML, JavaScript, and thelike. In some embodiments, a member device within member devices 502-504may be specifically programmed by either Java, .Net, QT, C, C++, Python,PHP and/or other suitable programming language. In some embodiment ofthe device software, device control may be distributed between multiplestandalone applications. In some embodiments, softwareaccessories/applications can be updated and redeployed remotely asindividual units or as a full software suite. In some embodiments, amember device may periodically report status or send alerts over text oremail. In some embodiments, a member device may contain a data recorderwhich is remotely downloadable by the user using network protocols suchas FTP, SSH, or other file transfer mechanisms. In some embodiments, amember device may provide several levels of user interface, for example,advance user, standard user. In some embodiments, one or more memberdevices within member devices 502-504 may be specifically programmedinclude or execute an application to perform a variety of possibletasks, such as, without limitation, messaging functionality, browsing,searching, playing, streaming or displaying various forms of content,including locally stored or uploaded messages, images and/or video,and/or games.

In some embodiments, the exemplary network 505 may provide networkaccess, data transport and/or other services to any computing devicecoupled to it. In some embodiments, the exemplary network 505 mayinclude and implement at least one specialized network architecture thatmay be based at least in part on one or more standards set by, forexample, without limitation, Global System for Mobile communication(GSM) Association, the Internet Engineering Task Force (IETF), and theWorldwide Interoperability for Microwave Access (WiMAX) forum. In someembodiments, the exemplary network 505 may implement one or more of aGSM architecture, a General Packet Radio Service (GPRS) architecture, aUniversal Mobile Telecommunications System (UMTS) architecture, and anevolution of UMTS referred to as Long Term Evolution (LTE). In someembodiments, the exemplary network 505 may include and implement, as analternative or in conjunction with one or more of the above, a WiMAXarchitecture defined by the WiMAX forum. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary network 505 may also include, for instance, at least oneof a local area network (LAN), a wide area network (WAN), the Internet,a virtual LAN (VLAN), an enterprise LAN, a layer 3 virtual privatenetwork (VPN), an enterprise IP network, or any combination thereof. Insome embodiments and, optionally, in combination of any embodimentdescribed above or below, at least one computer network communicationover the exemplary network 505 may be transmitted based at least in parton one of more communication modes such as but not limited to: NFC,RFID, Narrow Band Internet of Things (NBIOT), ZigBee, Z-Wave, Long RangeWide Area Network (LoRaWAN), open radio access network (ORAN), 3G, 4G,5G, GSM, GPRS, WiFi, WiMax, CDMA, OFDM, OFDMA, LTE, satellite and anycombination thereof. In some embodiments, the exemplary network 505 mayalso include mass storage, such as network attached storage (NAS), astorage area network (SAN), a content delivery network (CDN) or otherforms of computer or machine readable media.

In some embodiments, the exemplary server 506 or the exemplary server507 may be a web server (or a series of servers) running a networkoperating system, examples of which may include but are not limited toApache on Linux or Microsoft IIS (Internet Information Services). Insome embodiments, the exemplary server 506 or the exemplary server 507may be used for and/or provide cloud and/or network computing. Althoughnot shown in FIG. 5 , in some embodiments, the exemplary server 506 orthe exemplary server 507 may have connections to external systems likeemail, SMS messaging, text messaging, ad content providers, etc. Any ofthe features of the exemplary server 506 may be also implemented in theexemplary server 507 and vice versa.

In some embodiments, one or more of the exemplary servers 506 and 507may be specifically programmed to perform, in non-limiting example, asauthentication servers, search servers, email servers, social networkingservices servers, Short Message Service (SMS) servers, Instant Messaging(IM) servers, Multimedia Messaging Service (MMS) servers, exchangeservers, photo-sharing services servers, advertisement providingservers, financial/banking-related services servers, travel servicesservers, or any similarly suitable service-base servers for users of themember computing devices 501-504.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, for example, one or more exemplary computingmember devices 502-504, the exemplary server 506, and/or the exemplaryserver 507 may include a specifically programmed software module thatmay be configured to send, process, and receive information using ascripting language, a remote procedure call, an email, a tweet, ShortMessage Service (SMS), Multimedia Message Service (MMS), instantmessaging (IM), an application programming interface, Simple ObjectAccess Protocol (SOAP) methods, Common Object Request BrokerArchitecture (CORBA), HTTP (Hypertext Transfer Protocol), REST(Representational State Transfer), SOAP (Simple Object TransferProtocol), MLLP (Minimum Lower Layer Protocol), or any combinationthereof.

FIG. 6 depicts a block diagram of another exemplary computer-basedsystem and platform 600 in accordance with one or more embodiments ofthe present disclosure. However, not all of these components may berequired to practice one or more embodiments, and variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of various embodiments of the presentdisclosure. In some embodiments, the member computing device 602 a,member computing device 602 b through member computing device 602 nshown each at least includes a computer-readable medium, such as arandom-access memory (RAM) 608 coupled to a processor 610 or FLASHmemory. In some embodiments, the processor 610 may executecomputer-executable program instructions stored in memory 608. In someembodiments, the processor 610 may include a microprocessor, an ASIC,virtual machine, software container (e.g., Docker or other suitablecontainer), and/or a state machine among other physical and/or virtualprocessing technologies or any combination thereof. In some embodiments,the processor 610 may include, or may be in communication with, media,for example computer-readable media, which stores instructions that,when executed by the processor 610, may cause the processor 610 toperform one or more steps described herein. In some embodiments,examples of computer-readable media may include, but are not limited to,an electronic, optical, magnetic, or other storage or transmissiondevice capable of providing a processor, such as the processor 610 ofthe member computing device 602 a, with computer-readable instructions.In some embodiments, other examples of suitable media may include, butare not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memorychip, ROM, RAM, an ASIC, a configured processor, all optical media, allmagnetic tape or other magnetic media, or any other medium from which acomputer processor can read instructions. Also, various other forms ofcomputer-readable media may transmit or carry instructions to acomputer, including a router, private or public network, or othertransmission device or channel, both wired and wireless. In someembodiments, the instructions may comprise code from anycomputer-programming language, including, for example, C, C++, VisualBasic, Java, Python, Perl, JavaScript, and etc.

In some embodiments, member computing devices 602 a through 602 n mayalso comprise a number of external or internal devices such as a mouse,a CD-ROM, DVD, a physical or virtual keyboard, a display, or other inputor output devices. In some embodiments, examples of member computingdevices 602 a through 602 n (e.g., clients) may be any type ofprocessor-based platforms that are connected to a network 606 such as,without limitation, personal computers, digital assistants, personaldigital assistants, smart phones, pagers, digital tablets, laptopcomputers, Internet appliances, and other processor-based devices. Insome embodiments, member computing devices 602 a through 602 n may bespecifically programmed with one or more application programs inaccordance with one or more principles/methodologies detailed herein. Insome embodiments, member computing devices 602 a through 602 n mayoperate on any operating system capable of supporting a browser orbrowser-enabled application, such as Microsoft™ Windows™, and/or Linux.In some embodiments, member computing devices 602 a through 602 n shownmay include, for example, personal computers executing a browserapplication program such as Microsoft Corporation's Internet Explorer™,Google Chrome, Apple Computer, Inc.'s Safari™, Mozilla Firefox, and/orOpera. In some embodiments, through the member computing client devices602 a through 602 n, user 612 a, user 612 b through user 612 n, maycommunicate over the exemplary network 606 with each other and/or withother systems and/or devices coupled to the network 606. As shown inFIG. 6 , exemplary server devices 604 and 613 may include processor 605and processor 614, respectively, as well as memory 617 and memory 616,respectively. In some embodiments, the server devices 604 and 613 may bealso coupled to the network 606. In some embodiments, one or more membercomputing devices 602 a through 602 n may be mobile clients.

In some embodiments, at least one database of exemplary databases 607and 615 may be any type of database, including a database managed by adatabase management system (DBMS). In some embodiments, an exemplaryDBMS-managed database may be specifically programmed as an engine thatcontrols organization, storage, management, and/or retrieval of data inthe respective database. In some embodiments, the exemplary DBMS-manageddatabase may be specifically programmed to provide the ability to query,backup and replicate, enforce rules, provide security, compute, performchange and access logging, and/or automate optimization. In someembodiments, the exemplary DBMS-managed database may be chosen fromOracle database, IBM DB2, Adaptive Server Enterprise, FileMaker,Microsoft Access, Microsoft SQL Server, MySQL, PostgreSQL, and a NoSQLimplementation. In some embodiments, the exemplary DBMS-managed databasemay be specifically programmed to define each respective schema of eachdatabase in the exemplary DBMS, according to a particular database modelof the present disclosure which may include a hierarchical model,network model, relational model, object model, or some other suitableorganization that may result in one or more applicable data structuresthat may include fields, records, files, and/or objects. In someembodiments, the exemplary DBMS-managed database may be specificallyprogrammed to include metadata about the data that is stored.

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be specifically configured to operate in a cloudcomputing/architecture 625 such as, but not limiting to: infrastructurea service (IaaS) 810, platform as a service (PaaS) 808, and/or softwareas a service (SaaS) 806 using a web browser, mobile app, thin client,terminal emulator or other endpoint 804. FIG. 7 and FIG. 8 illustrateschematics of exemplary implementations of the cloudcomputing/architecture(s) in which the exemplary inventivecomputer-based systems/platforms, the exemplary inventive computer-baseddevices, and/or the exemplary inventive computer-based components of thepresent disclosure may be specifically configured to operate.

Referring now also to FIG. 9 , an exemplary method 900 for facilitatinggeneration of a solar roof design according to embodiments of thepresent disclosure is shown. Any of the systems, devices, componentry,models, software, hardware, or any combination thereof, may be utilizedto perform the steps of the method 900. In certain embodiments, themethod 900 may include, at 902, receiving at least one digital image ofa roof of a structure. In certain embodiments, at 902, the method 900may include receiving any type of content taken of the roof of astructure. For example, the content may include, but is not limited to,video content, audio content, virtual reality content, augmented realitycontent, any type of content, or any combination thereof. In certainembodiments, the roof may include, but is not limited to including,shingles, tiles, wireways, photovoltaic modules and/or systems, trusses,joists, the roof deck, insulation layers, roofing accessories, or anycombination thereof. In certain embodiments, the structure may includeany type of building, house, vessel, any type of structure, or anycombination thereof. At 904, the method 900 may include modelling atleast one roof plane of the roof in the at least one digital image togenerate a roof plane model. At 906, the method 900 may includedetermining a plurality of dimensions of the at least one roof planebased on the roof plane model.

At 908, the method 900 may include retrieving roofing accessory data ina roofing accessory database. In certain embodiments, the roofingaccessory data may include, but is not limited to, a plurality of solarroofing accessory part identifiers. In certain embodiments, each solarroofing accessory part identifier may identify each solar roofingaccessory part of a plurality of solar roofing accessory parts. Incertain embodiments, the roofing accessory data may also include, but isnot limited to, a plurality of solar roofing accessory part performancecharacteristics. In certain embodiments, each solar roofing accessorypart performance characteristic may be associated with each solarroofing accessory part of the plurality of solar roofing accessoryparts. At step 910, the method 900 may include simulating a plurality ofcandidate roof layouts for the at least one roof plane. In certainembodiments, the simulating may be based on the plurality of dimensionsof the at least one roof plane and the plurality of solar roofingaccessory parts, wherein each candidate roof layout of the pluralitycandidate roof layouts comprises a plurality of roof segments. Incertain embodiments, the plurality of roof segments may include aplurality of first type roof segments having a plurality of solarroofing accessory parts and a plurality of second type roof segmentshaving a plurality of non-solar roofing accessory parts.

At step 912, the method 900 may include analyzing the plurality ofcandidate roof layouts based on the plurality of first type roofsegments having the plurality of solar roofing accessory parts and theplurality of second type roof segments having the plurality of non-solarroofing accessory parts to assign a particular utilization prediction toeach candidate roof layout of the plurality of candidate roof layouts togenerate a plurality of utilization predictions. In certain embodiments,the particular utilization prediction of each candidate roof layout maybe based on at least one installation metric for installing eachcandidate roof layout and at least one solar roofing accessory partperformance characteristic associated with each solar roofing accessorypart. At step 914, the method 900 may include determining a particularroof layout from the plurality of candidate roof layouts based at leastin part on the plurality of utilization predictions associated with theplurality of candidate roof layouts. In certain embodiments, the rooflayout may include a plurality of selected solar roofing accessory partsassociated with the plurality of solar roofing parts of the particularroof layout and a plurality of selected non-solar roofing accessoryparts of the plurality of non-solar roofing accessory parts.

At step 916, the method 900 may include generating a solar roof designbased at least in part on the particular roof layout. In certainembodiments, the solar roof design may include, but is not limited to, alist of solar roofing accessory part identifiers, identifying eachselected solar roofing accessory part of the plurality of selected solarroofing accessory parts of the particular roof layout so as to trackeach selected solar roofing accessory part during a lifetime of a roofcompleted based at least in part on the solar roof design. At step 918,the method 900 may include instructing at least one computing device todisplay the solar roof design. Notably, the method 900 may is notlimited to the specific sequence of steps shown in FIG. 9 and mayinclude any of the features and functionality described in the presentdisclosure.

As used herein, the term “dynamically” and term “automatically,” andtheir logical and/or linguistic relatives and/or derivatives, mean thatcertain events and/or actions can be triggered and/or occur without anyhuman intervention. In some embodiments, events and/or actions inaccordance with the present disclosure can be in real-time and/or basedon a predetermined periodicity of at least one of: nanosecond, severalnanoseconds, millisecond, several milliseconds, second, several seconds,minute, several minutes, hourly, several hours, daily, several days,weekly, monthly, etc.

In some embodiments, exemplary inventive, specially programmed computingsystems and platforms with associated devices are configured to operatein the distributed network environment, communicating with one anotherover one or more suitable data communication networks (e.g., theInternet, satellite, etc.) and utilizing one or more suitable datacommunication protocols/modes such as, without limitation, IPX/SPX,X.25, AX.25, AppleTalk™, TCP/IP (e.g., HTTP), near-field wirelesscommunication (NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G,4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and othersuitable communication modes.

In some embodiments, the NFC can represent a short-range wirelesscommunications technology in which NFC-enabled devices are “swiped,”“bumped,” “tap” or otherwise moved in close proximity to communicate. Insome embodiments, the NFC could include a set of short-range wirelesstechnologies, typically requiring a distance of 10 cm or less. In someembodiments, the NFC may operate at 13.56 MHz on ISO/IEC 18000-3 airinterface and at rates ranging from 106 kbit/s to 424 kbit/s. In someembodiments, the NFC can involve an initiator and a target; theinitiator actively generates an RF field that can power a passivetarget. In some embodiment, this can enable NFC targets to take verysimple form factors such as tags, stickers, key fobs, or cards that donot require batteries. In some embodiments, the NFC's peer-to-peercommunication can be conducted when a plurality of NFC-enable devices(e.g., smartphones) within close proximity of each other.

The material disclosed herein may be implemented in software or firmwareor a combination of them or as instructions stored on a machine-readablemedium, which may be read and executed by one or more processors. Amachine-readable medium may include any medium and/or mechanism forstoring or transmitting information in a form readable by a machine(e.g., a computing device). For example, a machine-readable medium mayinclude read only memory (ROM); random access memory (RAM); magneticdisk storage media; optical storage media; flash memory devices;electrical, optical, acoustical or other forms of propagated signals(e.g., carrier waves, infrared signals, digital signals, etc.), andothers.

As used herein, the terms “computer engine” and “engine” identify atleast one software accessory and/or a combination of at least onesoftware accessory and at least one hardware accessory which aredesigned/programmed/configured to manage/control other software and/orhardware accessories (such as the libraries, software development kits(SDKs), objects, etc.).

Examples of hardware elements may include processors, microprocessors,circuits, circuit elements (e.g., transistors, resistors, capacitors,inductors, and so forth), integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), logicgates, registers, semiconductor device, chips, microchips, chip sets,and so forth. In some embodiments, the one or more processors may beimplemented as a Complex Instruction Set Computer (CISC) or ReducedInstruction Set Computer (RISC) processors; x86 instruction setcompatible processors, multi-core, or any other microprocessor orcentral processing unit (CPU). In various implementations, the one ormore processors may be dual-core processor(s), dual-core mobileprocessor(s), and so forth.

Computer-related systems, computer systems, and systems, as used herein,include any combination of hardware and software. Examples of softwaremay include software accessories, programs, applications, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces,application program interfaces (API), instruction sets, computer code,computer code segments, words, values, symbols, or any combinationthereof. Determining whether an embodiment is implemented using hardwareelements and/or software elements may vary in accordance with any numberof factors, such as desired computational rate, power levels, heattolerances, processing cycle budget, input data rates, output datarates, memory resources, data bus speeds and other design or performanceconstraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that make the logic or processor. Of note, various embodimentsdescribed herein may, of course, be implemented using any appropriatehardware and/or computing software languages (e.g., C++, Objective-C,Swift, Java, JavaScript, Python, Perl, QT, etc.).

In some embodiments, as detailed herein, one or more of thecomputer-based systems of the present disclosure may obtain, manipulate,transfer, store, transform, generate, and/or output any digital objectand/or data unit (e.g., from inside and/or outside of a particularapplication) that can be in any suitable form such as, withoutlimitation, a file, a contact, a task, an email, a message, a map, anentire application (e.g., a calculator), data points, and other suitabledata. In some embodiments, as detailed herein, one or more of thecomputer-based systems of the present disclosure may be implementedacross one or more of various computer platforms such as, but notlimited to: (1) FreeBSD, NetBSD, OpenBSD; (2) Linux; (3) MicrosoftWindows™; (4) OpenVMS™; (5) OS X (MacOS™); (6) UNIX™; (7) Android; (8)iOS™; (9) Embedded Linux; (10) Tizen™; (11) WebOS™; (12) Adobe AIR™;(13) Binary Runtime Environment for Wireless (BREW™); (14) Cocoa™ (API);(15) Cocoa™ Touch; (16) Java™ Platforms; (17) JavaFX™; (18) QNX™; (19)Mono; (20) Google Blink; (21) Apple WebKit; (22) Mozilla Gecko™; (23)Mozilla XUL; (24) .NET Framework; (25) Silverlight™; (26) Open WebPlatform; (27) Oracle Database; (28) Qt™; (29) SAP NetWeaver™; (30)Smartface™; (31) Vexi™; (32) Kubernetes™ and (33) Windows Runtime(WinRT™) or other suitable computer platforms or any combinationthereof. In some embodiments, illustrative computer-based systems orplatforms of the present disclosure may be configured to utilizehardwired circuitry that may be used in place of or in combination withsoftware instructions to implement features consistent with principlesof the disclosure. Thus, implementations consistent with principles ofthe disclosure are not limited to any specific combination of hardwarecircuitry and software. For example, various embodiments may be embodiedin many different ways as a software accessory such as, withoutlimitation, a stand-alone software package, a combination of softwarepackages, or it may be a software package incorporated as a “tool” in alarger software product.

In some embodiments, illustrative computer-based systems or platforms ofthe present disclosure may be configured to handle numerous concurrentusers that may be, but is not limited to, at least 100 (e.g., but notlimited to, 100-999), at least 1,000 (e.g., but not limited to,1,000-9,999), at least 10,000 (e.g., but not limited to, 10,000-99,999),at least 100,000 (e.g., but not limited to, 100,000-999,999), at least1,000,000 (e.g., but not limited to, 1,000,000-9,999,999), at least10,000,000 (e.g., but not limited to, 10,000,000-99,999,999), at least100,000,000 (e.g., but not limited to, 100,000,000-999,999,999), atleast 1,000,000,000 (e.g., but not limited to,1,000,000,000-999,999,999,999), and so on.

In some embodiments, illustrative computer-based systems or platforms ofthe present disclosure may be configured to output to distinct,specifically programmed graphical user interface implementations of thepresent disclosure (e.g., a desktop, a web app., etc.). In variousimplementations of the present disclosure, a final output may bedisplayed on a displaying screen which may be, without limitation, ascreen of a computer, a screen of a mobile device, or the like. Invarious implementations, the display may be a holographic display. Invarious implementations, the display may be a transparent surface thatmay receive a visual projection. Such projections may convey variousforms of information, images, or objects. For example, such projectionsmay be a visual overlay for a mobile augmented reality (MAR)application.

As used herein, the term “mobile device,” or the like, may refer to anyportable electronic device that may or may not be enabled with locationtracking functionality (e.g., MAC address, Internet Protocol (IP)address, or the like). For example, a mobile electronic device caninclude, but is not limited to, a mobile phone, Personal DigitalAssistant (PDA), Blackberry™, Pager, Smartphone, or any other reasonablemobile electronic device.

As used herein, terms “proximity detection,” “locating,” “locationdata,” “location information,” and “location tracking” refer to any formof location tracking technology or locating method that can be used toprovide a location of, for example, a particular computing device,system or platform of the present disclosure and any associatedcomputing devices, based at least in part on one or more of thefollowing techniques and devices, without limitation: accelerometer(s),gyroscope(s), Global Positioning Systems (GPS); GPS accessed usingBluetooth™; GPS accessed using LoRaWAN, or GPS accessed using anyreasonable form of wireless and non-wireless communication; WiFi™ serverlocation data; LoRaWAN based location data; Ultra-Wide Band (UWB) basedlocation data; Bluetooth™ based location data; triangulation such as,but not limited to, network based triangulation, WiFi™ serverinformation based triangulation, Bluetooth™ server information basedtriangulation; Cell Identification based triangulation, Enhanced CellIdentification based triangulation, Uplink-Time difference of arrival(U-TDOA) based triangulation, Time of arrival (TOA) based triangulation,Angle of arrival (AOA) based triangulation; techniques and systems usinga geographic coordinate system such as, but not limited to, longitudinaland latitudinal based, geodesic height based, Cartesian coordinatesbased; Radio Frequency Identification such as, but not limited to, Longrange RFID, Short range RFID; using any form of RFID tag such as, butnot limited to active RFID tags, passive RFID tags, battery assistedpassive RFID tags; or any other reasonable way to determine location.For ease, at times the above variations are not listed or are onlypartially listed; this is in no way meant to be a limitation.

As used herein, terms “cloud,” “Internet cloud,” “cloud computing,”“cloud architecture,” and similar terms correspond to at least one ofthe following: (1) a large number of computers connected through areal-time communication network (e.g., Internet); (2) providing theability to run a program or application on many connected computers(e.g., physical machines, virtual machines (VMs)) at the same time; (3)network-based services, which appear to be provided by real serverhardware, and are in fact served up by virtual hardware (e.g., virtualservers), simulated by software running on one or more real machines(e.g., allowing to be moved around and scaled up (or down) on the flywithout affecting the end user).

As used herein, the term “user” shall have a meaning of at least oneuser. In some embodiments, the terms “user”, “subscriber” “consumer” or“customer” should be understood to refer to a user of an application orapplications as described herein and/or a consumer of data supplied by adata provider. By way of example, and not limitation, the terms “user”or “subscriber” can refer to a person who receives data provided by thedata or service provider over the Internet in a browser session or canrefer to an automated software application which receives the data andstores or processes the data.

The aforementioned examples are, of course, illustrative and notrestrictive.

At least some aspects of the present disclosure will now be describedwith reference to the following numbered clauses.

Publications cited throughout this document are hereby incorporated byreference in their entirety. While one or more embodiments of thepresent disclosure have been described, it is understood that theseembodiments are illustrative only, and not restrictive, and that manymodifications may become apparent to those of ordinary skill in the art,including that various embodiments of the inventive methodologies, theillustrative systems and platforms, and the illustrative devicesdescribed herein can be utilized in any combination with each other.Further still, the various steps may be carried out in any desired order(and any desired steps may be added and/or any desired steps may beeliminated).

1. A method for providing a roof to a structure, the method comprising:determining a plurality of roofing accessory parts based at least inpart on using at least one processor to automatically generate a list ofroofing accessory part identifiers for the roof of the structure,wherein the at least one processor is configured to automaticallygenerate the list of roofing accessory part identifiers by: simulating aplurality of candidate roof layouts for at least one roof plane of therroof, based at least in part on: a plurality of dimensions of the atleast one roof plane; and a plurality of solar roofing accessory parts;wherein each candidate roof layout of the plurality candidate rooflayouts comprises a plurality of roof segments; wherein the plurality ofroof segments comprises: a plurality of first type roof segments havinga plurality of first type roofing accessory parts of the plurality ofroofing accessory parts; and a plurality of second type roof segmentshaving a plurality of second type roofing accessory parts of theplurality of roofing accessory parts; analyzing the plurality ofcandidate roof layouts based at least in part on the plurality of firsttype roof segments having the plurality of first type roofing accessoryparts and the plurality of second type roof segments having theplurality of second type roofing accessory parts to assign a particularutilization prediction to each candidate roof layout of the plurality ofcandidate roof layouts to generate a plurality of utilizationpredictions; wherein the particular utilization prediction of eachcandidate roof layout is based at least in part on: at least oneinstallation metric for installing each candidate roof layout; and atleast one roofing accessory part performance characteristic associatedwith each roofing accessory part; determining a particular roof layoutfrom the plurality of candidate roof layouts based at least in part onthe plurality of utilization predictions associated with the pluralityof candidate roof layouts; wherein the particular roof layout comprises:a plurality of selected first type roofing accessory parts associatedwith the plurality of first type roofing accessory parts; and aplurality of selected second type roofing accessory parts of theplurality of second type roofing accessory parts; generating a finalroof design for the roof based at least in part on the particular rooflayout; and wherein the final roof design comprises the list of roofingaccessory part identifiers, identifying, each selected first typeroofing accessory part of the plurality of selected first type roofingaccessory parts; and each selected second type roofing accessory part ofthe plurality of selected second type roofing accessory parts; andproviding the roof to the structure based on the final roof design. 2.The method as recited in claim 1, further comprising: instructing, bythe processor, at least one computing device to display the final roofdesign.
 3. The method as recited in claim 1, further comprising:determining, by the processor, full-device placements in the at leastone first type roof segment based at least in part on the plurality offirst type roofing accessory parts and the dimensions of the at leastone roof plane; wherein the full-device placements represent a firstarrangement of the plurality of first type roofing accessory parts thatfit within in the at least one first type roof segment; determining, bythe processor, partial-device placements in the at least one second typeroof segment based at least in part on the plurality of first typeroofing accessory parts and the dimensions of the at least one roofplane; and wherein the partial-device placements represent positions onthe at least one roof plane that fit a portion of the plurality of firsttype roofing accessory parts.
 4. The method as recited in claim 1,wherein the plurality of second type roofing accessory parts compriseasphalt shingles.
 5. The method as recited in claim 1, furthercomprising: generating, by the processor, estimates of a labor cost andan accessory cost associated with installing the plurality of selectedfirst type roofing accessory parts according to the first type roofdesign.
 6. The method as recited in claim 1, further comprising:determining, by the processor, an installation time needed to installeach selected first type roofing accessory part of the plurality ofselected first type roofing accessory parts according to the first typeroof design.
 7. The method as recited in claim 1, further comprising:determining, by the processor, a quantity of the plurality of secondtype roofing accessory parts of the first type roof design based atleast in part on particular roof layout; and generating, by theprocessor, a bill-of-materials representing the quantity of theplurality of second type roofing accessory parts of the first type roofdesign.
 8. The method as recited in claim 1, wherein each first typeroofing accessory part performance characteristic associated with eachfirst type roofing accessory part of the plurality of first type roofingaccessory parts comprises a part-specific first type efficiency metric.9. The method as recited in claim 1, further comprising: receiving, bythe processor, at least one digital image comprising light detection andranging measurements of the roof of the structure; and generating, bythe processor, a three-dimensional model of the roof based at least inpart on the light detection and ranging measurements.
 10. The method asrecited in claim 1, further comprising: determining, by the processor, ageographic location associated with the structure; determining, by theprocessor, a geographic orientation of the at least one roof plane basedat least in part on the geographic location and the at least one digitalimage; and scoring, by the processor, the plurality of candidate rooflayouts based at least in part on the geographic orientation and theplurality of selected first type roofing accessory parts.
 11. The methodas recited in claim 1, further comprising: determining, by theprocessor, at least one obstruction over the at least one roof planebased at least in part on the at least one digital image; and analyzing,by the processor, the plurality of candidate roof layouts based at leastin part on the at least one obstruction and the plurality of selectedfirst type roofing accessory parts.
 12. The method as recited in claim1, further comprising: receiving, by the at least one processor, anupdated first type roofing accessory part performance characteristicassociated with a particular first type roofing accessory part of theplurality of first type roofing accessory parts; wherein the updatedfirst type roofing accessory part performance characteristic associatedwith a particular first type roofing accessory part comprises at leastone user input indicating a change to the first type roofing accessorypart performance characteristic associated with the particular firsttype roofing accessory part; and updating, by the at least oneprocessor, a record associated with the particular first type roofingaccessory part to indicate the updated first type roofing accessory partperformance characteristic; and wherein the record is stored in aroofing accessory database.
 13. A system comprising: at least oneprocessor configured to execute software instructions, wherein thesoftware instructions, when executed, cause that least one processor toperform steps to: determine a plurrality of roofing accessory based atleast in part on using at least one processor to automatically generatea list of roofing accessory part identifiers for the roof of thestructure, wherein the at least one processor is configured toautomatically generate the list of roofing accessory part identifiersby: simulate a plurality of candidate roof layouts for the at least oneroof plane of the roof, based at least in part on: a plurality ofdimensions of the at least one roof plane and a plurality of roofingaccessory parts; wherein each candidate roof layout of the pluralitycandidate roof layouts comprises a plurality of roof segments; whereinthe plurality of roof segments comprises: a plurality of first type roofsegments having a plurality of first type roofing accessory parts of theplurality of roofing accessory parts; and a plurality of second typeroof segments having a plurality of second type roofing accessory partsof the plurality of roofing accessory parts; analyze the plurality ofcandidate roof layouts based at least in part on the plurality of firsttype roof segments having the plurality of first type roofing accessoryparts and the plurality of second type roof segments having theplurality of second type roofing accessory parts to assign a particularutilization prediction to each candidate roof layout of the plurality ofcandidate roof layouts to generate a plurality of utilizationpredictions; wherein the particular utilization prediction of eachcandidate roof layout is based at least in part on: at least oneinstallation metric for installing each candidate roof layout and atleast one solar roofing accessory part performance characteristicassociated with each solar roofing accessory part; determine aparticular roof layout from the plurality of candidate roof layoutsbased at least in part on the plurality of utilization predictionsassociated with the plurality of candidate roof layouts; wherein theparticular roof layout comprises: a plurality of selected first typeroofing accessory parts associated with the plurality of first typeroofing accessory parts and a plurality of selected second type roofingaccessory parts of the plurality of second type roofing accessory parts;generate a final roof design for the roof based at least in part on theparticular roof layout; wherein the final roof design comprises a listof roofing accessory part identifiers, identifying; each selected firsttype roofing accessory part of the plurality of selected first typeroofing accessory parts and each selected second type roofing accessorypart of the plurality of selected second type roofing accessory parts;and providing the roof to the structure based on the final roof design.14. The system as recited in claim 13, wherein the softwareinstructions, when executed, further cause that least one processor toperform steps to: determine full-device placements in the at least onefirst type roof segment based at least in part on the plurality of firsttype roofing accessory parts and the dimensions of the at least one roofplane; wherein the full-device placements represent a first arrangementof the plurality of first type roofing accessory parts that fit withinin the at least one first type roof segment; determine partial-deviceplacements in the at least one second type roof segment based at leastin part on the plurality of first type roofing accessory parts and thedimensions of the at least one roof plane; and wherein thepartial-device placements represent positions on the at least one roofplane that fit a portion of the plurality of first type roofingaccessory parts.
 15. The system as recited in claim 13, wherein theplurality of second type roofing accessory parts comprise asphaltshingles.
 16. The system as recited in claim 13, wherein the softwareinstructions, when executed, further cause that least one processor toperform steps to: generate estimates representing a labor cost and anaccessory cost associated with installing the plurality of selectedfirst type roofing accessory parts according to the first type roofdesign.
 17. The system as recited in claim 13, wherein the softwareinstructions, when executed, further cause that least one processor toperform steps to: determine an installation time needed to install eachselected first type roofing accessory part of the plurality of selectedfirst type roofing accessory parts according to the first type roofdesign.
 18. The system as recited in claim 13, wherein the softwareinstructions, when executed, further cause that least one processor toperform steps to: determine a quantity of the plurality of second typeroofing accessory parts of the first type roof design based at least inpart on particular roof layout; and generate a bill-of-materialsrepresenting the quantity of the plurality of second type roofingaccessory parts of the first type roof design.
 19. The system as recitedin claim 13, wherein each first type roofing accessory part performancecharacteristic associated with each first type roofing accessory part ofthe plurality of first type roofing accessory parts comprises apart-specific first type efficiency metric.
 20. The system as recited inclaim 13, wherein the software instructions, when executed, furthercause that least one processor to perform steps to: receive at least onedigital image comprising light detection and ranging measurements of theroof of the structure; and generate a three-dimensional model of theroof based at least in part on the light detection and rangingmeasurements.
 21. The system as recited in claim 13, wherein thesoftware instructions, when executed, further cause that least oneprocessor to perform steps to: determine a geographic locationassociated with the structure; determine a geographic orientation of theat least one roof plane based at least in part on the geographiclocation and the at least one digital image; and score the plurality ofcandidate roof layouts based at least in part on the geographicorientation and the plurality of selected first type roofing accessoryparts.
 22. The system as recited in claim 13, wherein the softwareinstructions, when executed, further cause that least one processor toperform steps to: determine at least one obstruction over the at leastone roof plane based at least in part on the at least one digital image;and analyze the plurality of candidate roof layouts based at least inpart on the at least one obstruction and the plurality of selected firsttype roofing accessory parts.
 23. The system as recited in claim 13,wherein the software instructions, when executed, further cause thatleast one processor to perform steps to: receive an updated first typeroofing accessory part performance characteristic associated with aparticular first type roofing accessory part of the plurality of firsttype roofing accessory parts; wherein the updated first type roofingaccessory part performance characteristic associated with a particularsolar roofing accessory part comprises at least one user inputindicating a change to the first type roofing accessory part performancecharacteristic associated with the particular first type roofingaccessory part; and update a record associated with the particular firsttype roofing accessory part to indicate the updated first type roofingaccessory part performance characteristic; and wherein the record isstored in a roofing accessory database.